Industrial Products – Jacob J. Thomson, Lisa Wong, Lucas B. Waltz, Crown Equipment Corp

Abstract for “Adjusting industrial vehicle performance”

“A method for changing vehicle performance parameters while performing an assigned task is disclosed. For an industrial vehicle, the process involves receiving a job description and converting it into a workflow modeling. The workflow model defines the tasks for the industrial vehicle. Associating the tasks to a kinematic modeling is another part of this process. The kinematic model can be generated by identifying the kinematic functions of an industrial vehicle, taking into account constraints that may be associated with the operation of the vehicle, and computing the cutback curve for a parameter in the kinematic. The workflow model is then modified using the kinematic model. Based on the applied Kinematic model to workflow model, the performance parameter of an industrial vehicle is modified.

Background for “Adjusting industrial vehicle performance”

The present disclosure concerns electronic systems for evaluating the performance of industrial vehicles, in particular, the evaluation of industrial cars based on simulations or empirical data or a combination thereof.

Wireless strategies are used by business operations such as distributors, retailers, manufacturers to increase efficiency and accuracy. These business operations may also deploy wireless strategies to reduce the negative effects of increasing labor and logistic costs.

“In a typical wireless implementation forklift trucks can be linked to a management software running on a corresponding computer company via wireless transceivers. Wireless transceivers serve as interfaces to the management systems to guide workers in their tasks. They can be used to instruct workers on how to move, stage, stage, or pick up items in a facility.

“A process for modifying vehicle performance parameters for industrial vehicles according to certain aspects of the present disclosure is provided. The process can modify vehicle performance parameters for one or several kinematic functions, while performing an assigned task in some embodiments. One example of this is where the process receives a job description for the industrial vehicle and simulates it. The job specification is then broken down into a workflow model. This model defines tasks for the industrial vehicle in an environment that allows it to perform an operation. The tasks are further associated with a Kinematic model. To generate the kinematic models, you identify the kinematic functions and environment of the industrial vehicles, receive constraints, compute a cutback curve for one parameter of a selected kinematic function, and then apply the kinematic model in the workflow model that is based on the job description to determine the results. Modifications to the vehicle performance parameters occur in response to simulating job specifications.

“Another aspect to the disclosed embodiments is receiving, for an industrial vehicle, job specifications and decomposing them into a workflow modeling, with the workflow model defining tasks for the industrial vehicle. In some embodiments, the process includes associating the tasks with an underlying kinematic model. The kinematic model can be generated by identifying the kinematic functions of an industrial vehicle, receiving constraints related to the operation of the vehicle, and computing the cutback curve for a parameter in the kinematic. The workflow model is then modified using the kinematic model. Based on the workflow model’s kinematic model, the performance parameter for the industrial vehicle can be modified.

“BRIEF DESCRIPTION ABOUT THE VIEWS FROM THE DRAWINGS”

“FIG. “FIG.

“FIG. FIG. 2 is a block diagram showing a data store. 1 according to certain aspects of the disclosure

“FIG. “FIG.

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“FIG. “FIG. 5. According to certain aspects of the disclosure

“FIG. “FIG.7 is a flowchart that illustrates a process for creating cutback curves according to aspects in the present disclosure.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG. 11. is a flowchart that illustrates a process to evaluate industrial performance according to aspects in the present disclosure.

“FIG. “FIG. 12 is a flowchart that illustrates a process to simulate a job specification according to aspects in the present disclosure.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG.

“FIGS. 16A-C illustrates graphical user interfaces to the fleet size estimation tool according to different aspects of the current disclosure.

“FIG. “FIG.

“The present disclosure provides systems, methods and computer-implemented techniques that allow industrial vehicles to be evaluated by simulating their performance. A first process is used to build a simulation structure specifically designed for the purpose of evaluating the performance of industrial vehicles. The first process uses a processor to create the simulation structure. It begins by delineating the tasks that are required to characterize an industrial vehicle’s performance and then builds a kinematic model to represent each task in the structure. This model takes into account energy and movement requirements. As described further below, the first process creates cutback curves which may be used by the processor to enhance the way a simulation runs. A processing engine can also build workflows into the simulation structure. Each workflow is a piecewise collection sequentially serialized tasks.

Once a simulation structure has been built, it is possible to use the structure to perform simulations of industrial vehicle performance. The job specification specifies the industrial vehicle performance to be simulated by the processing engine. The processing engine extracts a job form the job description and then aggregates them into a piecewise collection sequentially serialized workflows to describe the extracted job. By applying the kinematic model, the serialized workflows that characterize the job are processed against the simulation structure. To modify the simulation results, you can also apply one or more cutbacks, if applicable. The simulation results are used to determine the performance of industrial vehicles.

“The simulation environment is dynamic. A job specification can affect and even dynamically change the simulation structure. Sometimes, the job description can direct the construction of a completely new structure. The job specification, or some aspects thereof, can be used as feedback in the creation or modification to the simulation structure. The simulation structure can also dictate job specifications, facilitating interaction between the creation and use of the simulation structure. The job description can also be scaled according to job type, time and number of industrial vehicles. The simulation structure can simulate one job for an industrial vehicle (e.g., a specific putaway operation by industrial vehicle X), but it can also be used to evaluate multiple jobs performed by industrial vehicles or fleets of industrial vehicles over a shift, week or year.

The simulation structure allows you to evaluate the effectiveness of optional or new features in a virtual environment. It can also be used to test the functions of vehicle components (e.g. to determine if a vehicle is suitable for a particular job or blend, or whether it is suitable for the job. To measure vehicle performance or a combination thereof. The simulation structure allows you to easily compare an existing industrial vehicle with a virtual or optimal model.

“Also, the simulation structure could represent an ideal? The simulation structure can also be used to create a model of a vehicle, such as a fully functioning industrial vehicle. The simulation structure could also be used to represent a virtual? A model of an industrial vehicle from a fleet. A virtual model of a vehicle can be used to account for wear and age. It also allows you to compare actual vehicle performance with a model (normalizing the results). Simulators can be performed using either an ideal or virtual model.

The simulation structure can be compared against constraints, options, and other parameters to gain insight into the performance of industrial vehicles. The results can be used to load control data into an information link device of a selected industrial vehicle. This is done, for example, to reconfigure an existing industrial vehicle or alter its performance. The processing engine can also use the simulation structure in order to impute or fill in any missing information, such as by solving for variables related to a job or filling in the route from a starting point to an endpoint.

“The kinematic models include components that simulate the movement of an industrial vehicle in accordance with the tasks assigned to it. A kinematic model may also include an energy consumption model, which models the vehicle’s energy characteristics when performing the tasks. Cutback curves can also be generated. The cutback curves take constraints or other limitations into consideration, including by way of example, vehicle capabilities, automation/semi-automation features, environmental constraints including particular geo-constraints, operational constraints, operator constraints, etc., that are used to augment simulation results under corresponding conditions. A cutback curve can be used to simulate a task in a specific area of a warehouse. However, the same cutback curve will not apply in another part of the warehouse.

The cutback curves alter vehicle performance parameters to one or more kinematic function while performing a task. As an example, cutback curves can account for environmental conditions (e.g. warehouse policy setting a speed limit), operating conditions (e.g. limiting speed as a function fork height), and characteristics of the vehicle operator (based on the level of skill, certifications etc. ), vehicle configuration state (e.g. to account for reduced speed achieved by automation control), combinations thereof.

“The technology of industrial vehicles is improved by the disclosures herein. This disclosure also enhances the technology for industrial vehicle operation and control. Further, the disclosure herein improves technology for industrial vehicle performance simulation and evaluation. This results in improved predictive vehicle performance that is not otherwise possible. The present disclosure addresses the technical problem of optimizing and evaluating industrial vehicle performance. The technical solution consists of a computer-generated simulation and/or evaluation that account for complex energy consumption. This is compared against kinematic performance, which includes suitability and capability for an anticipated use, and environmental constraints.

The results of the industrial vehicle performances evaluations are typical of modern engineering work. They provide a realistic prediction (or validation) of the performance and energy consumption of an actual industrial vehicle in real-world conditions. Kinematic performance and energy consumption are also evaluated. This allows industrial vehicles can be designed, tuned, and optimized to work in their intended environment.

“The technical solutions provided herein enable an industrial vehicle manufacturer to determine whether a certain number of industrial vehicles (with optional technology) will be capable of handling a predetermined throughput in an environment like a warehouse before they actually deploy the industrial vehicles. The disclosure also allows for the evaluation of industrial vehicles already in use. It can detect even the smallest deviations from optimal performance, whether it be energy, kinematic, or combination thereof. This allows the vehicle to be tuned or electronically modified to improve its performance to achieve/re-acquire an improved performance capability.

“The technical solutions described herein have a technical significance in that they improve industrial vehicle evaluation and/or simulating, and allow for a wider range of configurations to virtually be tested and evaluated for suitability before deploying one or more of these vehicles into an environment. Advanced verification of industrial vehicle performance including measures of fitness and kinematic response (including speed), consumption versus conservation, as well as complex combinations thereof would not be possible without the technical solutions provided herein. From the technical solutions herein, complex scenarios can be evaluated before deployment, resulting in qualified selection from many different fleet configurations/environment configurations.”

“System Overview:”

“Referring now and especially to FIG. A system 100 is shown according to different aspects of the present disclosure. The illustrated system 100 represents a special purpose computing environment, or particular, that contains a number of hardware processing devices. They are identified generally by reference 102 and linked together by one (or more) networks. These network(s) are identified generally by reference 104.

“The network(s), 104 provides communications connections between the various processing device(s), 102. It may also be supported by networking components, 106 that interconnect processing devices, such as routers, hubs firewalls, firewalls, network interfaces wired or wireless communications links, and corresponding interconnections. The network(s), 104 can also include connections using one or several intranets or extranets. These may include local area networks, wide area networks, wireless networks, Wi-Fi networks, and corresponding interconnections.

A processing device 102 is a device that can be used as a server or personal computer, laptop, netbook, computer, purpose-driven device, special purpose computing device, and/or any other device capable of communicating with the network 104. There are many other types of processing devices 102, such as personal data assistant processors (PDA), palm computers, cellular device including smart phones and cellular mobile telephones, as well tablet computers.

“A processing device 102 is also provided on at minimum one industrial vehicle (108), such as a forklift truck or reach truck, stock picker truck, turret tractor, tow truck, tow tractor and rider pallet truck. The example configuration shows that the processing device (102) on each industrial vehicle (108) wirelessly communicates with one or more access points 110 to the corresponding networking component (106) which acts as a connection to network 104. Alternately, the industrial vehicles can be outfitted with WiFi, cell or any other suitable technology to allow the processing device on the industrial vehicle 110 to communicate with a remote device (e.g. over the networks 104)

Optional environmental based location tracking can be added to one or more industrial vehicles 108. This allows for the determination of the position of the industrial vehicle even in indoor environments where a GPS is not possible. Environmental based location tracking is a method to map and track an industrial vehicle’s location 108 within a restricted environment (e.g. a warehouse).

The server 112 in the illustrative 100 supports domain-level data processing and management within an environment. The server 112 can interact with industrial vehicles 108 to facilitate messaging, control, provide industrial vehicles with domain-level resources, and store data related industrial vehicle encounters.

“A simulation structure is used to evaluate the performance of industrial vehicles, as we will see. At least one processing device (102) includes an analysis engine, 114 and the corresponding data sources (collectively data sources 116). The server 112. executes the analysis engine 114 as well as the corresponding data source 116 in the first example implementation. An alternative implementation of the analysis engine 114 is that it is executed by the server 112. Another example implementation is to distribute the process, e.g. so that the server 112 can access a deeper and richer data set, as well as local data stored on remote devices 102 (e.g. a tablet computer or a desktop computer), as well as the processing device 102 on the corresponding industrial vehicle.108.

“For example, the processes can be implemented on a handheld device like a tablet using a Model View Controller architecture to implement the user interface. The processes described herein can also be executed as a passive model or a stateless controller. The model is passive and immutable because the user interacts with the corresponding graphical user interface without creating new data or selecting a configuration. Alternate configurations allow the process to dynamically compute certain or all of the evaluations. These computations can be done on the fly on an industrial vehicle 108 or a processing device 101.

Referring to FIG. “Referring to FIG. 2, the data sources 116 in an exemplary implementation include a collection databases that store different types of information. One or more of these data sources may not need to be used depending on the implementation. These data sources 116 do not have to be located together. The data sources 116 are databases that tie processes to the benefit of an enterprise from multiple domains.

“In the illustrated case, data sources 116 include an Industrial Vehicle Management Data Source 202 (supporting processes that execute in an industrial vehicles operation domain, e.g. by interfacing with an information linking device on an industry vehicle 108, as described with reference FIG. 3), a warehouse management software (WMS), 204 (supporting processes in the WMS domain related to movement and tracking goods within the operating environment), an information linking device on an industrial vehicle 108 (as described with reference to FIG. This list is not complete and is meant to be an illustration only.

“But, there is an industrial vehicle data source 220. The industrial vehicle data supply 210 contains data about the vehicle that can impact the vehicle’s dynamics and energy consumption. These data can be used in conjunction with the configuration parameters. Some parameters can be set as ‘Fixed’. Another example is that some parameters might have different values depending on whether an industrial vehicle has been loaded or unloaded. Different processing techniques can be used to distinguish options within a given situation (e.g. linear interpolation, statistical analyses, etc.). “To obtain the value that will be used, you must first distinguish between options within a given condition.

“An example implementation is one or more categories of parameters. In practice each category of parameters will have multiple parameters/variables. The parameter categories include both energy and kinematic measurements. A Traction category may include variables such as the maximum acceleration rate, battery current required to accelerate, maximum speed, maximum deceleration rate, and battery current needed to maintain that speed. If you are simulating a function of traction that is less than its maximum value, simulation results can be obtained using a predetermined curve, interpolation, average, or some other function to scale the maximum value.

“Basically, any feature, capability or limitation of an industrial vehicle can be described as a parameter. An exhaustive list of parameters is not possible because different types of industrial vehicles have different capabilities and characteristics. In certain instances, the data stored in the industrial device data source 210 represents ideal/optimal parameters values for a particular type of industrial vehicle. For example, all sit-down counterbalance truck share the same parameters. Alternate configurations allow the parameter values to be saved as an instance of a particular vehicle. This allows for the identification of new and old vehicles, as well as differences in parameters between different vehicle types. These differences can be caused by differences in the parameter values of vehicle instances within a fleet. For example, an information linking device attached to an industrial vehicle may measure differences. 3.”

“The data sources may also include an Environmental Model 212 (also referred herein as a Warehouse Model). Alternately, the Environmental model 212 could be part of geo-data number 208. The Environmental model 212 is a map of a dimensionally restricted environment. It may include a portion of a warehouse.

“In another example, the Environmental model212 includes a map (or at most a portion) of a warehouse where industrial vehicles operate. The Environmental model212 maps the dimensions of the rack structure used for storage items like pallets of goods and travel lanes. The Environmental model 212 may optionally include the expected weight/dimensions of the products that are stored in the rack. The Environmental model 212 can also be linked with WMS data 244 to enable the analysis engine (114) to extract actual weights from live inventory. The Environmental model 212 also has the option of storing both virtualized and actual live information. This allows for greater flexibility in the analysis explorations, simulations, as well as the process of executing them. The Environmental model212 can include environment-based operational restrictions, such as speed limits, speed limits, speed limits, defined restricted zones, permitted areas, geo-zones, and so on.

“An Operations Model 214 describes how industrial vehicle operators use them, how shifts are organized and how much product they move. Similar to the models in FIG. 2, it can be virtual, ideal, actual or a combination thereof. 2 can include virtual, ideal or actual data, as with the other models described in FIG.

A data source can also contain workflow parameters 216 which are used to build a workflow model. The workflow parameters are used to describe the execution of workflows in an aisle. They include the direction of travel, the order and number of locations for a stock picking workflow, and so on. Here are some examples of workflow parameters that were used to create workflow model 216.

“Yet, further, the data sources also include evaluation models 218. The evaluation models 218 also include a kinematics modeling 220, which will be explained in more detail below. The kinematics model consists of a drive, raise, load handler traverse, pivot, and other components. An industrial vehicle energy consumption model 222 is also included in the evaluation models 218. Data collected from the data sources 116 can be used to derive the kinematics model 220- and energy consumption model 220, respectively.

“As we have noted, evaluation models 218 may include virtualized and actual measured information from a fleet of industrial vehicles or a combination thereof.”

“Industrial Vehicle:”

“Referring FIG. “Referring to FIG. 1).”

The information linking device 302 contains the circuitry necessary to implement wireless communication, data processing and information processing. It also allows for wired (and optionally wireless!) communication with components of the industrial vehicle. The information linking device 302 contains a transceiver (304) for wireless communication. A single transceiver (304) is shown for simplicity, but in reality, there may be several wireless communication technologies. The transceiver can communicate with remote servers, such as server 112 in FIG. 1, via 802.11.xx across access points 110 in FIG. 1. Optionally, the transceiver 304 can support wireless communication such as Bluetooth, cellular, or infrared (IR), or any combination thereof. The transceiver can, for example, use a cell to IP bridge to send cellular signals to a remote server.

“The information linking device 302 also includes a control module 306, which is a processor and memory that implements computer instructions. This includes actions related to methods and processes or aspects thereof as described further herein. The control module 306 can communicate with the native electronic components of an industrial vehicle, making it a specific machine that is different from a general-purpose computer. The control module 306 uses the transceiver to exchange information with remote server 112 (FIG. 1) to control operation of the industrial vehicles 108, and for remote storage of information from the industrial vehicles, etc.”

“The information linking device 302.” The control module 306 controls the vehicle power enabling circuitry 308 to enable or disable industrial vehicles 108 or certain components. The control module 306 can, for example, control the power enabling circuitry of an industrial vehicle 308 so that power is provided to selected components of the industrial vehicles 108 via power cable 310. This could be based on operator login, geo-features detected, etc.

“But, further, the information connecting device 302 also includes a monitoring output (I/O), module 312 that allows for wired or wireless communication to peripheral devices attached or otherwise mounted to the industrial vehicle. These include sensors, meters encoders switches, etc. (collectively referred to as reference number 314) Other devices may be connected to the module 312 as well, e.g. third-party devices 316 like RFID scanners, meters, displays, meters, or other devices. This allows control module 306 access to the industrial vehicle 108 and process the information.

“The information linking device 302 communicates and/or is coupled with other components of the industrial vehicle system via a suitable vehicle bus 318. Any wired or wireless bus, network, or other communication capability that allows electronic components of an industrial vehicle 108 to interact with one another is called the vehicle network bus 318. The vehicle network bus 318 could include a controller area network bus (CAN), Local Interconnect Networks (LIN), time-triggered protocol (TTP) and other appropriate communication technologies.

“As we will see, seamless integration of control module 206 and other parts of the information linking device 318 into the native electronics of an industrial vehicle 108 is possible as a result of vehicle network bus 318, which allows for the use of the vehicle network bus. The control module 306 from the information linking device 302 communicates with, understands, and can communicate with native vehicle electronic components such as traction controllers or hydraulic controllers. ” (collectively, referred to as reference 320).

“Environmental-Based Tracking”

“According to further aspects of this disclosure, an environmental-based location tracking device 322 is installed on the industrial vehicle. The environmental based location tracker 322 can be added to other implementations. The vehicle electronics are connected to the environmental based tracking device 322 via the vehicle network bus 318, (e.g., the CAN bus). The environmental based location track device 322 can be connected to the vehicle electronics via the vehicle network bus 318 (e.g., CAN bus). It can also communicate with controllers and other modules of the industrial vehicle 108. The industrial vehicle 108 can spatially know its location in a constrained space, such as a warehouse.

“In the cases described further herein, a traditional technology such as a global position system (GPS), is unlikely to work indoors. The environmental-based location tracking device 322 may include a local awareness system, which uses markers such as RFID, beacons or lights to provide spatial awareness in the warehouse environment. To determine the location of an industrial vehicle, the environmental based tracking system 322 can also use transponders or a triangulation calculation. The environmental based location tracker 322 may use combinations of these and/or other technologies in order to determine the current position (real-time), of an industrial vehicle. In certain cases, the position can be continuously determined (e.g. every second or less). Alternately, other sampling intervals are possible to be determined to continuously determine the position of industrial vehicles over time (e.g. at discrete time intervals. Periodic or otherwise constant and repetitive time intervals. Intervals based on interrupts, triggers, or other measures).

“The environmental-based location tracking system 322 can also utilize knowledge read from inertial sensor, vehicle sensors and encoders. To determine the location of the industrial vehicle (108) within the warehouse, and/or to augment/modify the position determination from 322. This allows the location-based tracking system to determine the exact position of the vehicle within a limited dimension, such as a mapped area of a warehouse, using the geo-data (208/212) of FIG. 2.”

“Simulation Structure including Flow Overview”

“FIG. “FIG. The system can use the simulation structure to perform simulations once it has been built. A job specification can also influence the construction of the simulation structure. FIG. 4 shows the computer-executed steps. The computer-executed processes of FIG. 4 can be executed using a hardware processor coupled with physical memory. In this case, the processor is programmed with program code stored in memory to execute the processes. FIG. The system of FIG. 4 can be executed using one or more instances 114 of the analysis engine on a corresponding processing unit 102 (e.g. server, tablet, industrial vehicle processor, etc.). As described in detail below, data sources 116 can be used. Processing can be performed on one computing device or distributed across multiple computing devices. For example, the processing can be done on a tablet or other device attached to an industrial vehicle that communicates with a server computer. FIG. 4 shows computer-executed processes. 4. can be executed on computer-readable hardware that stores machine executable program code. The program code instructs a CPU to execute the described computer-executed process.

FIG. “From the ground up, FIG. 4 will be described. To show how a simulation structure can be created. Next, FIG. 4. This diagram illustrates non-limiting ways that the simulation structure can be used to simulate various industrial vehicles performing different tasks.

“Building a Simulation Building”

“As shown in the block diagram 400, to create a simulation structure, kinematic function 402 is defined. These functions describe a range of kinematic capabilities an industrial vehicle can perform. Practically, some kinematic functions are vehicle- and/or type-specific. The kinematic functions of a vehicle include driving, lifting/lowering the load handling device of the vehicle (e.g. the forks), load-handler traversing (e.g. which are present on large man-up industrial vehicles such as a Turret Stock Picker), and pivoting functions (e.g. rotating the direction relative to the operator’s chamber of the load handling function (e.g. the forks).

“Parameters associated the kinematic function 402, other data from data sources 404 (e.g. data source 116 in FIGS. These parameters, or any combination thereof, can be used to create cutbacks curves 406 which may modify certain instances of the kinematic function 402 during execution of a simulation. The processing engine 416 may use cutback curves 406 to enhance the way a simulation runs. If necessary, the processing engine 416 may use cutback curves 406 to modify the simulation results.

FIGS. 8-10 provide more detail on the “Cutback curves 406.” 8-10. Each cutback curve 406 computes a parameter for a selected kinematic function in the industrial vehicle. In this example, the kinematic function could be?drive? or?driver and raise? (where travel speed and fork height can be combined). The designated parameter could be travel speed or fork height. It may also include braking. The associated parameter can then be linked to one or more related parameters. These may be in one dimension.

A cutback curve 406 is derived from one or several constraints in the environment where the industrial vehicle operates. This information can be extracted from data sources 404. A cutback curve can be used to establish speed versus distance constraints, by delineating a speed envelope. An example of an implementation is that an entire aisle in a warehouse can be represented as a total distance along the abscissa curve of a speed-vs-distance cutback curve. An EAC (environmental end of aisle control) designates a speed zone at the warehouse aisle’s ends. The abscissa consists of a first segment that defines a distance from the start of the aisle EAC, a second segment (the middle length of the aisle) which designates no speed restriction distance and a third segment that defines the end of the aisle EAC distance. Speed is represented by the ordinate. The plot is created by identifying the lines that represent the maximum EAC speed of the vehicle and the line that represents the vehicle’s maximum speed. The plot is generated if the vehicle is stationary. It represents the maximum acceleration (or any other program) of the vehicle to reach the maximum EAC speed. The vehicle’s speed is restricted until it reaches the end of the EAC zone. After reaching the first EAC zone, the vehicle can accelerate to maximum speed but must halt to allow it to reach the end of aisle EAC zones at a speed not exceeding the EAC speed limit. This cutback curve can also be linked to a geolocation (e.g. coordinates X and Y corresponding to aisle 1). This cutback curve can also be dynamically modified. The cutback curve can be dynamically modified to alter such things as the fork height, weight, and braking rate. This cutback curve can also be tied to a vehicle, vehicle type or operator, set coordinates, a period of time, etc.

“Yet another thing is that this cutback curve could be used as a combined? cutback curve. Operator training might impose speed restrictions on the vehicle which are higher than the maximum allowed EAC speed but lower than the maximum vehicle speed. A combined cutback curve can be generated by placing each cutback curve on the same coordinate axis and selecting the minimum cutback curves at the points along that common coordinate axis (the abscissa). This simplified example is only for illustration. Cutback curves, which can include combined cutbacks, can be generated for any kinematic parameter using multiple dimensions.

A fork-height cutback curve can be used to modify a blending operation, which is the simultaneous performance by two or more truck functions (e.g., travel and fork elevation adjustment). Another way to modify the speed vs. height curve is to receive one or more constraints from the environment in which the industrial vehicle operates. The geo-limited height cutback, which is limited by operator skill and time, can be used to limit the speed vs. fork height curve. You can also express the fork height cutback in terms of distance, time, or any other measure. A combined height cutback is defined as the sum of all points along the common coordinate axis of two or more curves.

“Cutbacks are also possible to be used for performance, braking, and battery/energy. It is also possible to have multiple cutback curves at one geo-location. The cutback curves are combined in this example. An overall cutback can be calculated by choosing the smallest of the combined curves at each point along the common coordinate axis. This concept can be used in complex cases to apply it along multiple common dimensions.

To create an industrial vehicle kinematic model, the kinematic functions 402, cutback curves 406 and data sources 404 are used. The environmental constraints 408 are typically stored with the data source 404. However, they are presented here for clarity and discussion to show their role in forming the kinematic 410. The kinematic model of an industrial vehicle (or type of industrial vehicle) provides a convenient way to describe models for the kinematic functions 402. These can be processed using the computed cutback curves 406 and environmental constraints 408, respectively, or combinations thereof. The kinematic model of an industrial vehicle also includes an energy consumption model. The energy consumption model allows simulations to take into account energy consumption as a function the task being simulated. The energy consumption model complements the kinematic model, allowing simulations to be focused on energy only, energy as a function task performance, task performance in relation to energy, or solely based on task performance.

“In this example, the kinematic model (410) includes a drive-and-raise model that is derived from a drive-and-raise kinematic function. A uniform acceleration model can simplify driving. The vehicle accelerates at a predetermined maximum acceleration (set by drive parameters) and then decelerates as late as is possible to reach a reduced speed limit. Dynamic acceleration, velocity and travel direction can also be used. Blending can be included in the drive and raise model. A?no mix? model, for example, would allow for blending. Configurations include raising/lowering the forks to a travel height, stopping at a destination and then adjusting the forks to the final height. A?start blend only? The vehicle can adjust the forks to reach a desired travel height while driving towards its final destination. The forks can be adjusted to the final height once they reach their destination. A?blend of both? configuration allows for travel and fork height to be adjusted simultaneously. You can adjust travel and fork height simultaneously in a?blend both? configuration. This allows you to adjust the final fork height simultaneously with arriving at your destination.

“Similar considerations could be applied to other parts of the kinematic 410, such as a load handler traverse, pivot, and vehicle energy consumption models.”

“Tasks 422 are defined and can be described (optionally only) using the kinematic model. This model is e.g., it’s based on one or more kinematic functions. An analogous workflow model 414 can be described (optionally only) using defined tasks 412. Practically, you can use the tasks 412 to create any number of workflow models. The workflow models must also include all the operations the user wants to simulate. A workflow can be described as a series of tasks in certain cases.

“Kinematic models can be used to model specific types of vehicles or specific industrial vehicles. There can be many kinematic models (410). There are likely to be many tasks 412 that are similar to all types of vehicles, such as driving. You may also find tasks 412 that are specific to certain vehicles/vehicle models.

“A processing engine 416 receives job specifications 418 when it is in use. These describe the operation that will be simulated. The workflow model 414 is used to define the operation. The job specification 418 also can be used to describe a travel route (including a starting point and an ending point) within the environment. The travel path can be used to apply environmental constraints in the context of the simulation. The travel path can also be used to determine if and when to apply cutback curves during simulation. In some embodiments, a travel path is a route that the industrial vehicle can take to get from one point to another. Other embodiments do not include the route. Instead, the processing engine 416 determines the route (e.g. based on energy consumption, shortest distance, etc

“The processing engine 416 uses a kinematic model (including cutback curves 406 and environmental constraints 408), the workflow model 414 and data sources 404 to simulate job to produce results of 420. The simulation is performed using environmental constraints 408, such as warehouse speed limits. Such limits may be geo-based, time/shift/operator-based, etc. Further, the cutback curves 406 can be applied to the simulation. These are described in greater detail here. In a height cutback, for example, the vehicle will be restricted to lower speeds the higher the forks. This relationship can be simplified to be piecewise linear in an example implementation. It is dependent on the constraints that the time it takes to drive the identified path. The height cutbacks can then be broken down into a time from the start constraint and time to the end constraint.

“In the example implementation, constraints will be obeyed reactively. For instance, when simulating a vehicle driving down an EAC-equipped aisle, the model won’t accelerate until it has reached the end EAC distance. The model could slow down once it reaches the end EAC distance. A look ahead can also cause the vehicle’s brake to be applied so that it is at its maximum speed as it enters the EAC zone.

“These results 420 are then used to evaluate an industrial vehicle that is associated with the kinematic 410. The “results” The “results” could be a simulation result or a fleet estimate result. It could also represent a comparison between an actual truck and an ideal simulated one, as well as an energy simulation, time simulation, etc.

“In certain embodiments, the requirements 422 are included for the simulation. A requirement 422 could be, for example, that the processing engine 416 produce energy-based results 422 A requirement 422 could also be for the processing engine 416 solve for a route, (e.g., depending on energy and time) From the beginning point to the ending point. As illustrated in FIG. The job specification 418 is not required. In some cases, however, the requirements 422 may be included in the job specification 418.

The requirements can also be used as constraints. Another example of a requirement 422, is performance scoring. This could be the ability to perform a set number of operations (e.g. several retrieves and putaways) in a specified time or with a reduced energy consumption. Practical implementations may include limitations to make sure that these constraints are feasible with the available fleet. The constraint(s), in this instance, can be expressed in a goal-based way, such as minimize time or minimize energy. A requirement 422 could also indicate that a specific operation must be performed on a particular shift.

“Example Simulation”

The processing engine 416 must have information about the industrial vehicle being simulated and the environment in order to process the job specification. Processing engine 416 might also be able to receive information about a hypothetical operator or information about an actual operator. It may also receive operational information and combinations thereof. These information are extracted from data sources 416 (i.e. the data sources 112 of FIG. 1. and FIG. 2).”

The job description can be summarized as follows: “A TSP6000 begins at the end aisle 1, drives down aisle 1, and picks up a pallet rack 5, slot 3. It then moves the pallet to aisle 2, slot 1. You can also base this job specification on hypothetical data. You can simulate an actual event by using the job specification, such as WMS information or industrial vehicle management information. As an example, the TSP6000 is equipped with an information linking unit 302, as shown in FIG. 3. The above-described job is performed within a warehouse. The environmental-based location tracking (222) and other sensors that can be read by the information linking device (302), can be used to determine the travel route and load information. Data from a WMS can also be used to determine the job of an industrial vehicle (see FIG. 2). Further, the job description can be created based on a combination of previously measured and hypothetical data.

“Regardless, the processing engine416 identifies this job specification as one instance of a?Putaway? Workflow? 414. The Putway workflow can then be broken down into the relevant tasks 412 (e.g. :”

“Start in a known position (start at end of aisle 1)

“Drive and Raise to an Outbound Slot (drive down the aisle 1 and raise to slot #3 at rack 5)

“Pivot (if necessary)”

“Slot Interaction (pickup a pallet from slot 3)”

“Gain Load”

“Traverse Out”

“Drive and Raise (drive up to aisle 2, rack 2)

Summary for “Adjusting industrial vehicle performance”

The present disclosure concerns electronic systems for evaluating the performance of industrial vehicles, in particular, the evaluation of industrial cars based on simulations or empirical data or a combination thereof.

Wireless strategies are used by business operations such as distributors, retailers, manufacturers to increase efficiency and accuracy. These business operations may also deploy wireless strategies to reduce the negative effects of increasing labor and logistic costs.

“In a typical wireless implementation forklift trucks can be linked to a management software running on a corresponding computer company via wireless transceivers. Wireless transceivers serve as interfaces to the management systems to guide workers in their tasks. They can be used to instruct workers on how to move, stage, stage, or pick up items in a facility.

“A process for modifying vehicle performance parameters for industrial vehicles according to certain aspects of the present disclosure is provided. The process can modify vehicle performance parameters for one or several kinematic functions, while performing an assigned task in some embodiments. One example of this is where the process receives a job description for the industrial vehicle and simulates it. The job specification is then broken down into a workflow model. This model defines tasks for the industrial vehicle in an environment that allows it to perform an operation. The tasks are further associated with a Kinematic model. To generate the kinematic models, you identify the kinematic functions and environment of the industrial vehicles, receive constraints, compute a cutback curve for one parameter of a selected kinematic function, and then apply the kinematic model in the workflow model that is based on the job description to determine the results. Modifications to the vehicle performance parameters occur in response to simulating job specifications.

“Another aspect to the disclosed embodiments is receiving, for an industrial vehicle, job specifications and decomposing them into a workflow modeling, with the workflow model defining tasks for the industrial vehicle. In some embodiments, the process includes associating the tasks with an underlying kinematic model. The kinematic model can be generated by identifying the kinematic functions of an industrial vehicle, receiving constraints related to the operation of the vehicle, and computing the cutback curve for a parameter in the kinematic. The workflow model is then modified using the kinematic model. Based on the workflow model’s kinematic model, the performance parameter for the industrial vehicle can be modified.

“BRIEF DESCRIPTION ABOUT THE VIEWS FROM THE DRAWINGS”

“FIG. “FIG.

“FIG. FIG. 2 is a block diagram showing a data store. 1 according to certain aspects of the disclosure

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG. 5. According to certain aspects of the disclosure

“FIG. “FIG.7 is a flowchart that illustrates a process for creating cutback curves according to aspects in the present disclosure.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG. 11. is a flowchart that illustrates a process to evaluate industrial performance according to aspects in the present disclosure.

“FIG. “FIG. 12 is a flowchart that illustrates a process to simulate a job specification according to aspects in the present disclosure.

“FIG. “FIG.

“FIG. “FIG.

“FIG. “FIG.

“FIGS. 16A-C illustrates graphical user interfaces to the fleet size estimation tool according to different aspects of the current disclosure.

“FIG. “FIG.

“The present disclosure provides systems, methods and computer-implemented techniques that allow industrial vehicles to be evaluated by simulating their performance. A first process is used to build a simulation structure specifically designed for the purpose of evaluating the performance of industrial vehicles. The first process uses a processor to create the simulation structure. It begins by delineating the tasks that are required to characterize an industrial vehicle’s performance and then builds a kinematic model to represent each task in the structure. This model takes into account energy and movement requirements. As described further below, the first process creates cutback curves which may be used by the processor to enhance the way a simulation runs. A processing engine can also build workflows into the simulation structure. Each workflow is a piecewise collection sequentially serialized tasks.

Once a simulation structure has been built, it is possible to use the structure to perform simulations of industrial vehicle performance. The job specification specifies the industrial vehicle performance to be simulated by the processing engine. The processing engine extracts a job form the job description and then aggregates them into a piecewise collection sequentially serialized workflows to describe the extracted job. By applying the kinematic model, the serialized workflows that characterize the job are processed against the simulation structure. To modify the simulation results, you can also apply one or more cutbacks, if applicable. The simulation results are used to determine the performance of industrial vehicles.

“The simulation environment is dynamic. A job specification can affect and even dynamically change the simulation structure. Sometimes, the job description can direct the construction of a completely new structure. The job specification, or some aspects thereof, can be used as feedback in the creation or modification to the simulation structure. The simulation structure can also dictate job specifications, facilitating interaction between the creation and use of the simulation structure. The job description can also be scaled according to job type, time and number of industrial vehicles. The simulation structure can simulate one job for an industrial vehicle (e.g., a specific putaway operation by industrial vehicle X), but it can also be used to evaluate multiple jobs performed by industrial vehicles or fleets of industrial vehicles over a shift, week or year.

The simulation structure allows you to evaluate the effectiveness of optional or new features in a virtual environment. It can also be used to test the functions of vehicle components (e.g. to determine if a vehicle is suitable for a particular job or blend, or whether it is suitable for the job. To measure vehicle performance or a combination thereof. The simulation structure allows you to easily compare an existing industrial vehicle with a virtual or optimal model.

“Also, the simulation structure could represent an ideal? The simulation structure can also be used to create a model of a vehicle, such as a fully functioning industrial vehicle. The simulation structure could also be used to represent a virtual? A model of an industrial vehicle from a fleet. A virtual model of a vehicle can be used to account for wear and age. It also allows you to compare actual vehicle performance with a model (normalizing the results). Simulators can be performed using either an ideal or virtual model.

The simulation structure can be compared against constraints, options, and other parameters to gain insight into the performance of industrial vehicles. The results can be used to load control data into an information link device of a selected industrial vehicle. This is done, for example, to reconfigure an existing industrial vehicle or alter its performance. The processing engine can also use the simulation structure in order to impute or fill in any missing information, such as by solving for variables related to a job or filling in the route from a starting point to an endpoint.

“The kinematic models include components that simulate the movement of an industrial vehicle in accordance with the tasks assigned to it. A kinematic model may also include an energy consumption model, which models the vehicle’s energy characteristics when performing the tasks. Cutback curves can also be generated. The cutback curves take constraints or other limitations into consideration, including by way of example, vehicle capabilities, automation/semi-automation features, environmental constraints including particular geo-constraints, operational constraints, operator constraints, etc., that are used to augment simulation results under corresponding conditions. A cutback curve can be used to simulate a task in a specific area of a warehouse. However, the same cutback curve will not apply in another part of the warehouse.

The cutback curves alter vehicle performance parameters to one or more kinematic function while performing a task. As an example, cutback curves can account for environmental conditions (e.g. warehouse policy setting a speed limit), operating conditions (e.g. limiting speed as a function fork height), and characteristics of the vehicle operator (based on the level of skill, certifications etc. ), vehicle configuration state (e.g. to account for reduced speed achieved by automation control), combinations thereof.

“The technology of industrial vehicles is improved by the disclosures herein. This disclosure also enhances the technology for industrial vehicle operation and control. Further, the disclosure herein improves technology for industrial vehicle performance simulation and evaluation. This results in improved predictive vehicle performance that is not otherwise possible. The present disclosure addresses the technical problem of optimizing and evaluating industrial vehicle performance. The technical solution consists of a computer-generated simulation and/or evaluation that account for complex energy consumption. This is compared against kinematic performance, which includes suitability and capability for an anticipated use, and environmental constraints.

The results of the industrial vehicle performances evaluations are typical of modern engineering work. They provide a realistic prediction (or validation) of the performance and energy consumption of an actual industrial vehicle in real-world conditions. Kinematic performance and energy consumption are also evaluated. This allows industrial vehicles can be designed, tuned, and optimized to work in their intended environment.

“The technical solutions provided herein enable an industrial vehicle manufacturer to determine whether a certain number of industrial vehicles (with optional technology) will be capable of handling a predetermined throughput in an environment like a warehouse before they actually deploy the industrial vehicles. The disclosure also allows for the evaluation of industrial vehicles already in use. It can detect even the smallest deviations from optimal performance, whether it be energy, kinematic, or combination thereof. This allows the vehicle to be tuned or electronically modified to improve its performance to achieve/re-acquire an improved performance capability.

“The technical solutions described herein have a technical significance in that they improve industrial vehicle evaluation and/or simulating, and allow for a wider range of configurations to virtually be tested and evaluated for suitability before deploying one or more of these vehicles into an environment. Advanced verification of industrial vehicle performance including measures of fitness and kinematic response (including speed), consumption versus conservation, as well as complex combinations thereof would not be possible without the technical solutions provided herein. From the technical solutions herein, complex scenarios can be evaluated before deployment, resulting in qualified selection from many different fleet configurations/environment configurations.”

“System Overview:”

“Referring now and especially to FIG. A system 100 is shown according to different aspects of the present disclosure. The illustrated system 100 represents a special purpose computing environment, or particular, that contains a number of hardware processing devices. They are identified generally by reference 102 and linked together by one (or more) networks. These network(s) are identified generally by reference 104.

“The network(s), 104 provides communications connections between the various processing device(s), 102. It may also be supported by networking components, 106 that interconnect processing devices, such as routers, hubs firewalls, firewalls, network interfaces wired or wireless communications links, and corresponding interconnections. The network(s), 104 can also include connections using one or several intranets or extranets. These may include local area networks, wide area networks, wireless networks, Wi-Fi networks, and corresponding interconnections.

A processing device 102 is a device that can be used as a server or personal computer, laptop, netbook, computer, purpose-driven device, special purpose computing device, and/or any other device capable of communicating with the network 104. There are many other types of processing devices 102, such as personal data assistant processors (PDA), palm computers, cellular device including smart phones and cellular mobile telephones, as well tablet computers.

“A processing device 102 is also provided on at minimum one industrial vehicle (108), such as a forklift truck or reach truck, stock picker truck, turret tractor, tow truck, tow tractor and rider pallet truck. The example configuration shows that the processing device (102) on each industrial vehicle (108) wirelessly communicates with one or more access points 110 to the corresponding networking component (106) which acts as a connection to network 104. Alternately, the industrial vehicles can be outfitted with WiFi, cell or any other suitable technology to allow the processing device on the industrial vehicle 110 to communicate with a remote device (e.g. over the networks 104)

Optional environmental based location tracking can be added to one or more industrial vehicles 108. This allows for the determination of the position of the industrial vehicle even in indoor environments where a GPS is not possible. Environmental based location tracking is a method to map and track an industrial vehicle’s location 108 within a restricted environment (e.g. a warehouse).

The server 112 in the illustrative 100 supports domain-level data processing and management within an environment. The server 112 can interact with industrial vehicles 108 to facilitate messaging, control, provide industrial vehicles with domain-level resources, and store data related industrial vehicle encounters.

“A simulation structure is used to evaluate the performance of industrial vehicles, as we will see. At least one processing device (102) includes an analysis engine, 114 and the corresponding data sources (collectively data sources 116). The server 112. executes the analysis engine 114 as well as the corresponding data source 116 in the first example implementation. An alternative implementation of the analysis engine 114 is that it is executed by the server 112. Another example implementation is to distribute the process, e.g. so that the server 112 can access a deeper and richer data set, as well as local data stored on remote devices 102 (e.g. a tablet computer or a desktop computer), as well as the processing device 102 on the corresponding industrial vehicle.108.

“For example, the processes can be implemented on a handheld device like a tablet using a Model View Controller architecture to implement the user interface. The processes described herein can also be executed as a passive model or a stateless controller. The model is passive and immutable because the user interacts with the corresponding graphical user interface without creating new data or selecting a configuration. Alternate configurations allow the process to dynamically compute certain or all of the evaluations. These computations can be done on the fly on an industrial vehicle 108 or a processing device 101.

Referring to FIG. “Referring to FIG. 2, the data sources 116 in an exemplary implementation include a collection databases that store different types of information. One or more of these data sources may not need to be used depending on the implementation. These data sources 116 do not have to be located together. The data sources 116 are databases that tie processes to the benefit of an enterprise from multiple domains.

“In the illustrated case, data sources 116 include an Industrial Vehicle Management Data Source 202 (supporting processes that execute in an industrial vehicles operation domain, e.g. by interfacing with an information linking device on an industry vehicle 108, as described with reference FIG. 3), a warehouse management software (WMS), 204 (supporting processes in the WMS domain related to movement and tracking goods within the operating environment), an information linking device on an industrial vehicle 108 (as described with reference to FIG. This list is not complete and is meant to be an illustration only.

“But, there is an industrial vehicle data source 220. The industrial vehicle data supply 210 contains data about the vehicle that can impact the vehicle’s dynamics and energy consumption. These data can be used in conjunction with the configuration parameters. Some parameters can be set as ‘Fixed’. Another example is that some parameters might have different values depending on whether an industrial vehicle has been loaded or unloaded. Different processing techniques can be used to distinguish options within a given situation (e.g. linear interpolation, statistical analyses, etc.). “To obtain the value that will be used, you must first distinguish between options within a given condition.

“An example implementation is one or more categories of parameters. In practice each category of parameters will have multiple parameters/variables. The parameter categories include both energy and kinematic measurements. A Traction category may include variables such as the maximum acceleration rate, battery current required to accelerate, maximum speed, maximum deceleration rate, and battery current needed to maintain that speed. If you are simulating a function of traction that is less than its maximum value, simulation results can be obtained using a predetermined curve, interpolation, average, or some other function to scale the maximum value.

“Basically, any feature, capability or limitation of an industrial vehicle can be described as a parameter. An exhaustive list of parameters is not possible because different types of industrial vehicles have different capabilities and characteristics. In certain instances, the data stored in the industrial device data source 210 represents ideal/optimal parameters values for a particular type of industrial vehicle. For example, all sit-down counterbalance truck share the same parameters. Alternate configurations allow the parameter values to be saved as an instance of a particular vehicle. This allows for the identification of new and old vehicles, as well as differences in parameters between different vehicle types. These differences can be caused by differences in the parameter values of vehicle instances within a fleet. For example, an information linking device attached to an industrial vehicle may measure differences. 3.”

“The data sources may also include an Environmental Model 212 (also referred herein as a Warehouse Model). Alternately, the Environmental model 212 could be part of geo-data number 208. The Environmental model 212 is a map of a dimensionally restricted environment. It may include a portion of a warehouse.

“In another example, the Environmental model212 includes a map (or at most a portion) of a warehouse where industrial vehicles operate. The Environmental model212 maps the dimensions of the rack structure used for storage items like pallets of goods and travel lanes. The Environmental model 212 may optionally include the expected weight/dimensions of the products that are stored in the rack. The Environmental model 212 can also be linked with WMS data 244 to enable the analysis engine (114) to extract actual weights from live inventory. The Environmental model 212 also has the option of storing both virtualized and actual live information. This allows for greater flexibility in the analysis explorations, simulations, as well as the process of executing them. The Environmental model212 can include environment-based operational restrictions, such as speed limits, speed limits, speed limits, defined restricted zones, permitted areas, geo-zones, and so on.

“An Operations Model 214 describes how industrial vehicle operators use them, how shifts are organized and how much product they move. Similar to the models in FIG. 2, it can be virtual, ideal, actual or a combination thereof. 2 can include virtual, ideal or actual data, as with the other models described in FIG.

A data source can also contain workflow parameters 216 which are used to build a workflow model. The workflow parameters are used to describe the execution of workflows in an aisle. They include the direction of travel, the order and number of locations for a stock picking workflow, and so on. Here are some examples of workflow parameters that were used to create workflow model 216.

“Yet, further, the data sources also include evaluation models 218. The evaluation models 218 also include a kinematics modeling 220, which will be explained in more detail below. The kinematics model consists of a drive, raise, load handler traverse, pivot, and other components. An industrial vehicle energy consumption model 222 is also included in the evaluation models 218. Data collected from the data sources 116 can be used to derive the kinematics model 220- and energy consumption model 220, respectively.

“As we have noted, evaluation models 218 may include virtualized and actual measured information from a fleet of industrial vehicles or a combination thereof.”

“Industrial Vehicle:”

“Referring FIG. “Referring to FIG. 1).”

The information linking device 302 contains the circuitry necessary to implement wireless communication, data processing and information processing. It also allows for wired (and optionally wireless!) communication with components of the industrial vehicle. The information linking device 302 contains a transceiver (304) for wireless communication. A single transceiver (304) is shown for simplicity, but in reality, there may be several wireless communication technologies. The transceiver can communicate with remote servers, such as server 112 in FIG. 1, via 802.11.xx across access points 110 in FIG. 1. Optionally, the transceiver 304 can support wireless communication such as Bluetooth, cellular, or infrared (IR), or any combination thereof. The transceiver can, for example, use a cell to IP bridge to send cellular signals to a remote server.

“The information linking device 302 also includes a control module 306, which is a processor and memory that implements computer instructions. This includes actions related to methods and processes or aspects thereof as described further herein. The control module 306 can communicate with the native electronic components of an industrial vehicle, making it a specific machine that is different from a general-purpose computer. The control module 306 uses the transceiver to exchange information with remote server 112 (FIG. 1) to control operation of the industrial vehicles 108, and for remote storage of information from the industrial vehicles, etc.”

“The information linking device 302.” The control module 306 controls the vehicle power enabling circuitry 308 to enable or disable industrial vehicles 108 or certain components. The control module 306 can, for example, control the power enabling circuitry of an industrial vehicle 308 so that power is provided to selected components of the industrial vehicles 108 via power cable 310. This could be based on operator login, geo-features detected, etc.

“But, further, the information connecting device 302 also includes a monitoring output (I/O), module 312 that allows for wired or wireless communication to peripheral devices attached or otherwise mounted to the industrial vehicle. These include sensors, meters encoders switches, etc. (collectively referred to as reference number 314) Other devices may be connected to the module 312 as well, e.g. third-party devices 316 like RFID scanners, meters, displays, meters, or other devices. This allows control module 306 access to the industrial vehicle 108 and process the information.

“The information linking device 302 communicates and/or is coupled with other components of the industrial vehicle system via a suitable vehicle bus 318. Any wired or wireless bus, network, or other communication capability that allows electronic components of an industrial vehicle 108 to interact with one another is called the vehicle network bus 318. The vehicle network bus 318 could include a controller area network bus (CAN), Local Interconnect Networks (LIN), time-triggered protocol (TTP) and other appropriate communication technologies.

“As we will see, seamless integration of control module 206 and other parts of the information linking device 318 into the native electronics of an industrial vehicle 108 is possible as a result of vehicle network bus 318, which allows for the use of the vehicle network bus. The control module 306 from the information linking device 302 communicates with, understands, and can communicate with native vehicle electronic components such as traction controllers or hydraulic controllers. ” (collectively, referred to as reference 320).

“Environmental-Based Tracking”

“According to further aspects of this disclosure, an environmental-based location tracking device 322 is installed on the industrial vehicle. The environmental based location tracker 322 can be added to other implementations. The vehicle electronics are connected to the environmental based tracking device 322 via the vehicle network bus 318, (e.g., the CAN bus). The environmental based location track device 322 can be connected to the vehicle electronics via the vehicle network bus 318 (e.g., CAN bus). It can also communicate with controllers and other modules of the industrial vehicle 108. The industrial vehicle 108 can spatially know its location in a constrained space, such as a warehouse.

“In the cases described further herein, a traditional technology such as a global position system (GPS), is unlikely to work indoors. The environmental-based location tracking device 322 may include a local awareness system, which uses markers such as RFID, beacons or lights to provide spatial awareness in the warehouse environment. To determine the location of an industrial vehicle, the environmental based tracking system 322 can also use transponders or a triangulation calculation. The environmental based location tracker 322 may use combinations of these and/or other technologies in order to determine the current position (real-time), of an industrial vehicle. In certain cases, the position can be continuously determined (e.g. every second or less). Alternately, other sampling intervals are possible to be determined to continuously determine the position of industrial vehicles over time (e.g. at discrete time intervals. Periodic or otherwise constant and repetitive time intervals. Intervals based on interrupts, triggers, or other measures).

“The environmental-based location tracking system 322 can also utilize knowledge read from inertial sensor, vehicle sensors and encoders. To determine the location of the industrial vehicle (108) within the warehouse, and/or to augment/modify the position determination from 322. This allows the location-based tracking system to determine the exact position of the vehicle within a limited dimension, such as a mapped area of a warehouse, using the geo-data (208/212) of FIG. 2.”

“Simulation Structure including Flow Overview”

“FIG. “FIG. The system can use the simulation structure to perform simulations once it has been built. A job specification can also influence the construction of the simulation structure. FIG. 4 shows the computer-executed steps. The computer-executed processes of FIG. 4 can be executed using a hardware processor coupled with physical memory. In this case, the processor is programmed with program code stored in memory to execute the processes. FIG. The system of FIG. 4 can be executed using one or more instances 114 of the analysis engine on a corresponding processing unit 102 (e.g. server, tablet, industrial vehicle processor, etc.). As described in detail below, data sources 116 can be used. Processing can be performed on one computing device or distributed across multiple computing devices. For example, the processing can be done on a tablet or other device attached to an industrial vehicle that communicates with a server computer. FIG. 4 shows computer-executed processes. 4. can be executed on computer-readable hardware that stores machine executable program code. The program code instructs a CPU to execute the described computer-executed process.

FIG. “From the ground up, FIG. 4 will be described. To show how a simulation structure can be created. Next, FIG. 4. This diagram illustrates non-limiting ways that the simulation structure can be used to simulate various industrial vehicles performing different tasks.

“Building a Simulation Building”

“As shown in the block diagram 400, to create a simulation structure, kinematic function 402 is defined. These functions describe a range of kinematic capabilities an industrial vehicle can perform. Practically, some kinematic functions are vehicle- and/or type-specific. The kinematic functions of a vehicle include driving, lifting/lowering the load handling device of the vehicle (e.g. the forks), load-handler traversing (e.g. which are present on large man-up industrial vehicles such as a Turret Stock Picker), and pivoting functions (e.g. rotating the direction relative to the operator’s chamber of the load handling function (e.g. the forks).

“Parameters associated the kinematic function 402, other data from data sources 404 (e.g. data source 116 in FIGS. These parameters, or any combination thereof, can be used to create cutbacks curves 406 which may modify certain instances of the kinematic function 402 during execution of a simulation. The processing engine 416 may use cutback curves 406 to enhance the way a simulation runs. If necessary, the processing engine 416 may use cutback curves 406 to modify the simulation results.

FIGS. 8-10 provide more detail on the “Cutback curves 406.” 8-10. Each cutback curve 406 computes a parameter for a selected kinematic function in the industrial vehicle. In this example, the kinematic function could be?drive? or?driver and raise? (where travel speed and fork height can be combined). The designated parameter could be travel speed or fork height. It may also include braking. The associated parameter can then be linked to one or more related parameters. These may be in one dimension.

A cutback curve 406 is derived from one or several constraints in the environment where the industrial vehicle operates. This information can be extracted from data sources 404. A cutback curve can be used to establish speed versus distance constraints, by delineating a speed envelope. An example of an implementation is that an entire aisle in a warehouse can be represented as a total distance along the abscissa curve of a speed-vs-distance cutback curve. An EAC (environmental end of aisle control) designates a speed zone at the warehouse aisle’s ends. The abscissa consists of a first segment that defines a distance from the start of the aisle EAC, a second segment (the middle length of the aisle) which designates no speed restriction distance and a third segment that defines the end of the aisle EAC distance. Speed is represented by the ordinate. The plot is created by identifying the lines that represent the maximum EAC speed of the vehicle and the line that represents the vehicle’s maximum speed. The plot is generated if the vehicle is stationary. It represents the maximum acceleration (or any other program) of the vehicle to reach the maximum EAC speed. The vehicle’s speed is restricted until it reaches the end of the EAC zone. After reaching the first EAC zone, the vehicle can accelerate to maximum speed but must halt to allow it to reach the end of aisle EAC zones at a speed not exceeding the EAC speed limit. This cutback curve can also be linked to a geolocation (e.g. coordinates X and Y corresponding to aisle 1). This cutback curve can also be dynamically modified. The cutback curve can be dynamically modified to alter such things as the fork height, weight, and braking rate. This cutback curve can also be tied to a vehicle, vehicle type or operator, set coordinates, a period of time, etc.

“Yet another thing is that this cutback curve could be used as a combined? cutback curve. Operator training might impose speed restrictions on the vehicle which are higher than the maximum allowed EAC speed but lower than the maximum vehicle speed. A combined cutback curve can be generated by placing each cutback curve on the same coordinate axis and selecting the minimum cutback curves at the points along that common coordinate axis (the abscissa). This simplified example is only for illustration. Cutback curves, which can include combined cutbacks, can be generated for any kinematic parameter using multiple dimensions.

A fork-height cutback curve can be used to modify a blending operation, which is the simultaneous performance by two or more truck functions (e.g., travel and fork elevation adjustment). Another way to modify the speed vs. height curve is to receive one or more constraints from the environment in which the industrial vehicle operates. The geo-limited height cutback, which is limited by operator skill and time, can be used to limit the speed vs. fork height curve. You can also express the fork height cutback in terms of distance, time, or any other measure. A combined height cutback is defined as the sum of all points along the common coordinate axis of two or more curves.

“Cutbacks are also possible to be used for performance, braking, and battery/energy. It is also possible to have multiple cutback curves at one geo-location. The cutback curves are combined in this example. An overall cutback can be calculated by choosing the smallest of the combined curves at each point along the common coordinate axis. This concept can be used in complex cases to apply it along multiple common dimensions.

To create an industrial vehicle kinematic model, the kinematic functions 402, cutback curves 406 and data sources 404 are used. The environmental constraints 408 are typically stored with the data source 404. However, they are presented here for clarity and discussion to show their role in forming the kinematic 410. The kinematic model of an industrial vehicle (or type of industrial vehicle) provides a convenient way to describe models for the kinematic functions 402. These can be processed using the computed cutback curves 406 and environmental constraints 408, respectively, or combinations thereof. The kinematic model of an industrial vehicle also includes an energy consumption model. The energy consumption model allows simulations to take into account energy consumption as a function the task being simulated. The energy consumption model complements the kinematic model, allowing simulations to be focused on energy only, energy as a function task performance, task performance in relation to energy, or solely based on task performance.

“In this example, the kinematic model (410) includes a drive-and-raise model that is derived from a drive-and-raise kinematic function. A uniform acceleration model can simplify driving. The vehicle accelerates at a predetermined maximum acceleration (set by drive parameters) and then decelerates as late as is possible to reach a reduced speed limit. Dynamic acceleration, velocity and travel direction can also be used. Blending can be included in the drive and raise model. A?no mix? model, for example, would allow for blending. Configurations include raising/lowering the forks to a travel height, stopping at a destination and then adjusting the forks to the final height. A?start blend only? The vehicle can adjust the forks to reach a desired travel height while driving towards its final destination. The forks can be adjusted to the final height once they reach their destination. A?blend of both? configuration allows for travel and fork height to be adjusted simultaneously. You can adjust travel and fork height simultaneously in a?blend both? configuration. This allows you to adjust the final fork height simultaneously with arriving at your destination.

“Similar considerations could be applied to other parts of the kinematic 410, such as a load handler traverse, pivot, and vehicle energy consumption models.”

“Tasks 422 are defined and can be described (optionally only) using the kinematic model. This model is e.g., it’s based on one or more kinematic functions. An analogous workflow model 414 can be described (optionally only) using defined tasks 412. Practically, you can use the tasks 412 to create any number of workflow models. The workflow models must also include all the operations the user wants to simulate. A workflow can be described as a series of tasks in certain cases.

“Kinematic models can be used to model specific types of vehicles or specific industrial vehicles. There can be many kinematic models (410). There are likely to be many tasks 412 that are similar to all types of vehicles, such as driving. You may also find tasks 412 that are specific to certain vehicles/vehicle models.

“A processing engine 416 receives job specifications 418 when it is in use. These describe the operation that will be simulated. The workflow model 414 is used to define the operation. The job specification 418 also can be used to describe a travel route (including a starting point and an ending point) within the environment. The travel path can be used to apply environmental constraints in the context of the simulation. The travel path can also be used to determine if and when to apply cutback curves during simulation. In some embodiments, a travel path is a route that the industrial vehicle can take to get from one point to another. Other embodiments do not include the route. Instead, the processing engine 416 determines the route (e.g. based on energy consumption, shortest distance, etc

“The processing engine 416 uses a kinematic model (including cutback curves 406 and environmental constraints 408), the workflow model 414 and data sources 404 to simulate job to produce results of 420. The simulation is performed using environmental constraints 408, such as warehouse speed limits. Such limits may be geo-based, time/shift/operator-based, etc. Further, the cutback curves 406 can be applied to the simulation. These are described in greater detail here. In a height cutback, for example, the vehicle will be restricted to lower speeds the higher the forks. This relationship can be simplified to be piecewise linear in an example implementation. It is dependent on the constraints that the time it takes to drive the identified path. The height cutbacks can then be broken down into a time from the start constraint and time to the end constraint.

“In the example implementation, constraints will be obeyed reactively. For instance, when simulating a vehicle driving down an EAC-equipped aisle, the model won’t accelerate until it has reached the end EAC distance. The model could slow down once it reaches the end EAC distance. A look ahead can also cause the vehicle’s brake to be applied so that it is at its maximum speed as it enters the EAC zone.

“These results 420 are then used to evaluate an industrial vehicle that is associated with the kinematic 410. The “results” The “results” could be a simulation result or a fleet estimate result. It could also represent a comparison between an actual truck and an ideal simulated one, as well as an energy simulation, time simulation, etc.

“In certain embodiments, the requirements 422 are included for the simulation. A requirement 422 could be, for example, that the processing engine 416 produce energy-based results 422 A requirement 422 could also be for the processing engine 416 solve for a route, (e.g., depending on energy and time) From the beginning point to the ending point. As illustrated in FIG. The job specification 418 is not required. In some cases, however, the requirements 422 may be included in the job specification 418.

The requirements can also be used as constraints. Another example of a requirement 422, is performance scoring. This could be the ability to perform a set number of operations (e.g. several retrieves and putaways) in a specified time or with a reduced energy consumption. Practical implementations may include limitations to make sure that these constraints are feasible with the available fleet. The constraint(s), in this instance, can be expressed in a goal-based way, such as minimize time or minimize energy. A requirement 422 could also indicate that a specific operation must be performed on a particular shift.

“Example Simulation”

The processing engine 416 must have information about the industrial vehicle being simulated and the environment in order to process the job specification. Processing engine 416 might also be able to receive information about a hypothetical operator or information about an actual operator. It may also receive operational information and combinations thereof. These information are extracted from data sources 416 (i.e. the data sources 112 of FIG. 1. and FIG. 2).”

The job description can be summarized as follows: “A TSP6000 begins at the end aisle 1, drives down aisle 1, and picks up a pallet rack 5, slot 3. It then moves the pallet to aisle 2, slot 1. You can also base this job specification on hypothetical data. You can simulate an actual event by using the job specification, such as WMS information or industrial vehicle management information. As an example, the TSP6000 is equipped with an information linking unit 302, as shown in FIG. 3. The above-described job is performed within a warehouse. The environmental-based location tracking (222) and other sensors that can be read by the information linking device (302), can be used to determine the travel route and load information. Data from a WMS can also be used to determine the job of an industrial vehicle (see FIG. 2). Further, the job description can be created based on a combination of previously measured and hypothetical data.

“Regardless, the processing engine416 identifies this job specification as one instance of a?Putaway? Workflow? 414. The Putway workflow can then be broken down into the relevant tasks 412 (e.g. :”

“Start in a known position (start at end of aisle 1)

“Drive and Raise to an Outbound Slot (drive down the aisle 1 and raise to slot #3 at rack 5)

“Pivot (if necessary)”

“Slot Interaction (pickup a pallet from slot 3)”

“Gain Load”

“Traverse Out”

“Drive and Raise (drive up to aisle 2, rack 2)

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