Adaptive acceleration for material handling vehicles

Abstract

Operating a material handling vehicle includes: monitoring, by a controller, a first vehicle driving parameter during a first manual operation of the vehicle by an operator; monitoring, by the controller, the first vehicle driving parameter during a second manual operation of the vehicle by the operator; receiving, by the controller, a request to implement semi-automatic driving operation after the first manual operation of the vehicle and the second manual operation of the vehicle; calculating, by the controller, a first weighted average value based on the first vehicle driving parameter monitored during the first manual operation of the vehicle and the first vehicle parameter monitored during the second manual operation of the vehicle; and controlling, by the controller, implementation of the semi-automatic driving operation based at least in part on the calculated first weighted average value.

Description

Background Art

[0001] Material handling vehicles are commonly used for picking goods in warehouses and distribution centers. These vehicles typically include a power unit and a load-handling assembly, which may include load-carrying forks. The vehicle also has control structures for controlling the operation and movement of the vehicle.

[0002] In a typical picking operation, an operator fills an order from available inventory items located in storage areas provided along one or more aisles of a warehouse or distribution center. The operator drives the vehicle between various picking locations for the item(s) to be picked. The operator can drive the vehicle either by using controls on the vehicle or via a wireless remote control device associated with the vehicle. Summary of the Invention

[0003] According to a first aspect of the present disclosure, a method for operating a material handling vehicle is provided, comprising: monitoring, by a controller, a first vehicle driving parameter during a first manual operation of the vehicle by an operator; monitoring, by the controller, the first vehicle driving parameter during a second manual operation of the vehicle by the operator; receiving, by the controller, a request to implement semi-automatic driving operation after the first manual operation of the vehicle and the second manual operation of the vehicle; calculating, by the controller, a first weighted average value based on the first vehicle driving parameter monitored during the first manual operation of the vehicle and the first vehicle parameter monitored during the second manual operation of the vehicle; and controlling, by the controller, implementation of the semi-automatic driving operation based at least in part on the calculated first weighted average value.

[0004] According to this aspect, calculating the first weighted average value may include: calculating a first component of the first weighted average value by applying a first weight value to a first processed value associated with a vehicle parameter monitored during a first manual operation of the vehicle; calculating a second component of the first weighted average value by applying a second weight value to a second processed value associated with a vehicle parameter monitored during a second manual operation of the vehicle; and calculating the first weighted average value based on the calculated first and second components. According to this aspect, the first weight value may be different from the second weight value. Furthermore, the second weight value may be greater than the first weight value.

[0005] According to this aspect, the second manual operation of the vehicle may occur closer in time to receiving the request to implement the semi-autonomous driving operation than the first manual operation.

[0006] According to the second aspect of the present disclosure, the monitored first vehicle parameter may correspond to a first driving direction of the vehicle.

[0007] Furthermore, according to a second aspect, the method may include simultaneously monitoring, by a controller, a second vehicle driving parameter corresponding to a second direction different from the first direction of travel during a first manual operation of the vehicle by an operator and a second manual operation of the vehicle. According to this aspect, the method may include calculating, by the controller, a second weighted average value based on the second vehicle driving parameter monitored during the first manual operation of the vehicle and the second vehicle parameter monitored during the monitored second manual operation of the vehicle. Furthermore, the first vehicle driving parameter may include acceleration in a first direction and the second vehicle driving parameter may include acceleration in a second direction, wherein the first direction and the second direction may be substantially orthogonal to each other.

[0008] According to aspects of the present disclosure, the method may include modifying the calculated first weighted average value based on the calculated second weighted average value when the calculated second weighted average value falls outside a predefined intermediate range.

[0009] Furthermore, based on the modified first weighted average value, the controller may control implementation of a semi-automatic driving operation, wherein controlling implementation of the semi-automatic driving operation includes limiting a maximum acceleration of the vehicle.

[0010] Another aspect of the present disclosure includes a system for operating a material handling vehicle, the system comprising: a memory storing executable instructions; and a processor in communication with the memory, wherein execution of the executable instructions by the processor causes the processor to: monitor a first vehicle driving parameter during a first manual operation of the vehicle by an operator; monitor the first vehicle driving parameter during a second manual operation of the vehicle by the operator; receive a request to implement semi-automatic driving operation after the first manual operation of the vehicle and the second manual operation of the vehicle; calculate a first weighted average based on the first vehicle driving parameter monitored during the first manual operation of the vehicle and the first vehicle parameter monitored during the second manual operation of the vehicle; and control the implementation of the semi-automatic driving operation based at least in part on the calculated first weighted average.

[0011] According to another aspect, calculating the first weighted average may include: calculating a first component of the first weighted average by applying a first weight value to a first processed value associated with a vehicle parameter monitored during a first manual operation of the vehicle; calculating a second component of the first weighted average by applying a second weight value to a second processed value associated with a vehicle parameter monitored during a second manual operation of the vehicle; and calculating the first weighted average based on the calculated first and second components. According to this aspect, the first weight value may be different from the second weight value. Furthermore, the second weight value may be greater than the first weight value.

[0012] According to this other aspect, the second manual operation of the vehicle may occur closer in time to receiving the request to enable semi-autonomous driving operation than the first manual operation.

[0013] According to yet another aspect of the present disclosure, the monitored first vehicle parameter may correspond to a first driving direction of the vehicle.

[0014] Furthermore, according to the above aspect, the system may include a processor that simultaneously monitors a second vehicle driving parameter corresponding to a second direction different from the first direction of travel during a first manual operation of the vehicle by an operator and a second manual operation of the vehicle by an operator. According to this aspect, the processor may calculate a second weighted average based on the second vehicle driving parameter monitored during the first manual operation of the vehicle and the second vehicle driving parameter monitored during the monitored second manual operation of the vehicle. Furthermore, the first vehicle driving parameter may include acceleration in a first direction and the second vehicle driving parameter may include acceleration in a second direction, wherein the first direction and the second direction may be substantially orthogonal to each other.

[0015] According to this further aspect, the system may include the processor modifying the calculated first weighted average value based on the calculated second weighted average value when the calculated second weighted average value falls outside a predefined intermediate range.

[0016] Furthermore, based on the modified first weighted average, the processor may control implementation of a semi-autonomous driving operation, wherein controlling implementation of the semi-autonomous driving operation includes limiting a maximum acceleration of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A and Figure 1B is an illustration of a material handling vehicle capable of remote wireless operation according to various aspects of the present disclosure;

[0018] Figure 2 is a schematic diagram of several components of a material handling vehicle capable of remote wireless operation according to various aspects of the present disclosure;

[0019] Figure 3 A flow chart depicting an example algorithm for monitoring at least one driving parameter during a first manual operation and a second manual operation of a vehicle, calculating a weighted average, and controlling implementation of a semi-autonomous driving operation based on the weighted average;

[0020] Figure 4 A flow chart depicting an example algorithm for calculating a first value indicative of acceleration of a vehicle in a first direction during a recent manual operation of the vehicle;

[0021] Figure 5illustrates a table containing non-realistic sample acceleration values ​​in a first direction corresponding to a recent manual operation of a vehicle;

[0022] Figure 6 The diagram shows the inclusion of wa x-i A table of sample values ​​of ;

[0023] Figure 7 A flow chart depicting an example algorithm for calculating a second value indicative of acceleration of a vehicle in a second direction during a recent manual operation of the vehicle;

[0024] Figure 8 illustrating a table containing non-realistic sample acceleration values ​​in a second direction corresponding to a recent manual operation of a vehicle;

[0025] Figure 9 The diagram shows the inclusion of wa y-i A table of sample values ​​of ;

[0026] Figure 10 The diagram shows a x-wa-max-i A table of sample values ​​of ;

[0027] Figure 11 The diagram shows a y-wa-max-i A table of sample values ​​of ;

[0028] Figure 12 A flow chart depicting an example algorithm for calculating a maximum acceleration to be used during a next semi-autonomous driving maneuver based on a calculated weighted average indicating acceleration of a vehicle in a first direction and a second direction during a previous manual operation of the vehicle; and

[0029] Figure 13 Plots the weighted average value (a y-max ) for three separate ranges. DETAILED DESCRIPTION

[0030] In the following detailed description of the illustrated embodiments, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration and not limitation, specific embodiments that may be practiced. It should be understood that other embodiments may be utilized and changes may be made without departing from the spirit and scope of the various embodiments of the present disclosure.

[0031] Low-level electric picking trucks

[0032] Referring now to the drawings, and in particular to the Figure 1A and Figure 1B, a material handling vehicle, illustrated as a low level order picking truck 10, generally includes a load handling assembly 12 extending from a power unit 14. The load handling assembly 12 includes a pair of forks 16, each fork 16 having a load-supporting wheel assembly 18. In addition to or in lieu of the illustrated arrangement of forks 16, the load handling assembly 12 may include other load handling features, such as a load backrest, scissors-type elevating forks, outriggers, or separate height-adjustable forks. Still further, the load handling assembly 12 may include load handling features, such as a mast, a load platform, a collection cage, or other support structure carried by the forks 16 or otherwise provided for handling a load supported and carried by the electric order picking truck 10 or pushed or pulled by the truck (i.e., such as by a trailer).

[0033] The illustrated power unit 14 includes a step-through operator's station 30 that separates a first end section 14A (opposite the forks 16) from a second end section 14B (proximate the forks 16) of the power unit 14. The step-through operator's station 30 provides a platform 32 on which an operator can stand to drive the truck 10 and / or provides a location from which the operator can operate various included features of the truck 10.

[0034] The first work area is disposed toward the first end section 14A of the power unit 14 and includes a control area 40 for driving the truck 10 and for controlling features of the load handling assembly 12 when the operator stands on the platform 32. The first end section 14A defines a compartment 48 for housing the battery, control electronics, including the controller 103 (see FIG. Figure 2 ), and (one or more) electric motors, such as traction motors, steering motors and lifting motors for the forks (not shown).

[0035] As shown for purposes of illustration and not limitation, the control area 40 includes a handle 52 for steering the truck 10, which may include controls such as handles, butterfly switches, thumb wheels, rocker switches, hand wheels, steering tillers, etc., for controlling the acceleration / braking and travel direction of the truck 10, see Figure 1A and Figure 1BFor example, as shown, a control such as a switch handle or limit switch 54 can be provided on the handle 52, which is spring-biased to a center neutral position. Rotating the limit switch 54 forward and upward will cause the truck 10 to move forward at an acceleration proportional to the amount of rotation of the limit switch 54, for example, the power unit first, until the truck 10 reaches a predefined maximum speed, at which point the truck 10 is no longer allowed to accelerate to a higher speed. For example, if the limit switch 54 is rotated very quickly by 50% of the maximum angle that the limit switch 54 can rotate, the truck 10 will accelerate at approximately 50% of the maximum acceleration that the truck is capable of until the truck reaches 50% of the maximum speed that the truck is capable of. It is also contemplated that the acceleration can be determined using an acceleration map stored in memory, wherein the rotation angle of the limit switch 54 is used as an input in the acceleration map and has corresponding acceleration values ​​in the acceleration map. The acceleration values ​​corresponding to the rotation angle of the limit switch in the acceleration map can be proportional to the rotation angle of the limit switch or vary in any desired manner. There may also be a speed map stored in memory, where the rotation angle of the limit switch 54 is used as an input in the speed map and has a corresponding maximum speed value stored in the speed map. For example, when the limit switch 54 is rotated 50% of the maximum angle that the limit switch 54 is capable of, the truck will accelerate at the corresponding acceleration value stored in the acceleration map to the maximum speed value stored in the speed map corresponding to the limit switch angle of 50% of the maximum angle. Similarly, rotating the limit switch 54 toward the rear of the truck 10 and downward will cause the truck 10 to move in the opposite direction at an acceleration proportional to the amount of rotation of the limit switch 54, for example, the forks first, until the truck 10 reaches a predefined maximum speed corresponding to the amount of rotation of the limit switch 54, at which point the truck 10 is no longer allowed to accelerate to a higher speed.

[0036] A presence sensor 58 may be provided to detect the presence of an operator on the truck 10. For example, the presence sensor 58 may be located on, above, or below the platform floor, or otherwise provided around the operator station 30. In the exemplary truck 10 of FIG. 1 , the presence sensors 58 are shown in dashed lines, indicating that they are located below the platform floor. In this arrangement, the presence sensors 58 may include load cells, switches, etc. Alternatively, the presence sensors 58 may be implemented above the platform floor, such as by using ultrasound, capacitance, a laser scanner, a camera, or other suitable sensing technology. The use of the presence sensors 58 will be described in more detail herein.

[0037] An antenna 66 extends vertically from the power unit 14 and is provided for receiving control signals from a corresponding wireless remote control device 70. It is also contemplated that the antenna 66 may be located within the compartment 48 of the power unit 14 or elsewhere on the truck 10. According to one embodiment, the vehicle 10 may include a pole (not shown) extending vertically from the power unit 14 and including the antenna 66, which is provided for receiving control signals from the corresponding wireless remote control device 70. The pole may include a light on top, such that the pole and light define a lighthouse. The remote control device 70 may include a transmitter worn or otherwise maintained by an operator. The remote control device 70 may be manually operated by the operator, for example, by pressing a button or other control, to cause the remote control device 70 to wirelessly transmit at least a first type of signal specifying a travel request to the truck 10. A travel request is a command requesting the corresponding truck 10 to travel a predetermined amount, as will be described in greater detail herein.

[0038] The truck 10 also includes one or more obstacle sensors 76 disposed around the truck 10, for example, at a first end section facing the power unit 14 and / or on the sides of the power unit 14. The obstacle sensors 76 include at least one non-contact obstacle sensor on the truck 10 and are operable to define at least one detection zone. For example, when the truck 10 is traveling in response to a travel request wirelessly received from the remote control device 70, the at least one detection zone may define an area at least partially forward of the truck 10 in a forward direction of travel.

[0039] The obstacle sensor 76 may include any suitable proximity detection technology capable of detecting the presence of an object / obstacle or capable of generating a signal that can be analyzed to detect the presence of an object / obstacle within (one or more) predefined detection areas of the power unit 14, such as an ultrasonic sensor, an optical recognition device, an infrared sensor, a laser scanner sensor, etc.

[0040] In practice, the truck 10 may be implemented in other formats, styles, and features, such as an end-control pallet truck including a steering tiller arm coupled to a tiller for steering the truck. Similarly, while the remote control device 70 is illustrated as a glove-like structure 70, a variety of implementations of the remote control device 70 may be implemented, including, for example, finger-worn, lanyard- or belt-mounted, etc. Furthermore, the truck, remote control system, and / or components thereof, including the remote control device 70, may include any additional and / or alternative features or implementations.

[0041] Control system for remote operation of low-level electric picking trucks

[0042] refer to Figure 2, a block diagram illustrates a control arrangement for integrating remote control commands with the truck 10. Antenna 66 is coupled to a receiver 102 for receiving commands issued by the remote control device 70. Receiver 102 passes received control signals to a controller 103, which implements an appropriate response to the received commands and, therefore, may also be referred to herein as a master controller. In this regard, controller 103 is implemented in hardware and may also execute software (including firmware, resident software, microcode, etc.). Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.

[0043] Therefore, controller 103 can comprise electronic controller, and it defines at least in part the data processing system that is suitable for storing and / or executing program code, and can comprise at least one processor that is directly or indirectly coupled to memory element by system bus or other suitable connection.Memory element can comprise the local memory used during the actual execution of program code, the memory integrated into microcontroller or application specific integrated circuit (ASIC), programmable gate array or other reconfigurable processing equipment etc.At least one processor can comprise any processing unit that can be operated to receive and execute instruction (such as program code from one or more memory elements).At least one processor can comprise receiving input data, processing this data by computer instruction and generating any type of device of output data.Such processor can be microcontroller, handheld device, laptop computer or notebook computer, desktop computer, microcomputer, digital signal processor (DSP), mainframe, server, cellular phone, personal digital assistant, other programmable computer device or its any combination.Such processor can also use programmable logic device such as field programmable gate array (FPGA) to realize, or alternatively, be realized as application specific integrated circuit (ASIC) or similar device.Term " processor " is also intended to contain two or more combinations in above-mentioned equipment, for example, two or more microcontrollers.

[0044] Depending on the logic implemented, the response implemented by the controller 103 in response to commands received wirelessly, for example, via the wireless transmitter and corresponding antenna 66 and receiver 102 of the remote control device 70, can include one or more actions or no action. Positive actions can include controlling, adjusting, or otherwise affecting one or more components of the truck 10. The controller 103 can also receive information from other inputs 104, for example, from sources such as presence sensors 58, obstacle sensors 76, switches, load sensors, encoders, and other devices / features available to the truck 10, to determine the appropriate action to take in response to the command received from the remote control device 70. The sensors 58, 76, etc. can be coupled to the controller 103 via the inputs 104 or via a suitable truck network, such as a control area network (CAN) bus 110.

[0045] The controller 103 is also capable of determining the vertical position, i.e., height, of the load handling assembly 12, including the forks 16, relative to the ground (e.g., the floor surface along which the truck 10 is traveling), as described below. One or more height sensors or switches may be provided in the second end section 14B of the power unit 14 that sense when the load handling assembly 12, including the forks 16, is vertically raised relative to the ground and / or a lower point on the first end section 14A of the power unit 14. For example, first, second, and third switches (not shown) may be provided within the second end section 14B to be activated by the load handling assembly 12. Figure 1A These switches are activated when the load handling assembly 12 is raised at the first, second and third vertical positions designated by the dashed lines 141A, 141B and 141C in FIG. The lowest position of the load handling assembly 12 can also be determined via the load sensor LS indicating zero weight.

[0046] In one embodiment, the controller 103 may include one or more accelerometers that can measure the physical acceleration of the truck 10 along one, two, or three axes. It is also contemplated that the accelerometer 1103 may be separate from the controller 103 but coupled to and in communication with the controller 103 to generate and transmit acceleration signals to the controller 103, see Figure 2. For example, the accelerometer 1103 can measure the acceleration of the truck 10 in the travel direction DT of the truck 10 (also referred to as the first travel direction herein), which in the embodiment of Figure 1 is collinear with the axis X, where the X axis can generally be parallel to the forks 16. The travel direction DT or first travel direction can be defined as the direction in which the truck 10 is moving, which can be a forward or power unit first direction, or a reverse or fork first direction. The accelerometer 1103 can also measure the acceleration of the truck 10 along a transverse direction TR (also referred to as the second direction herein) that is approximately 90 degrees to the travel direction DT of the truck 10, which is collinear with the Y axis in the embodiment of Figure 1. The accelerometer 1103 can also measure the acceleration of the truck 10 in another direction transverse to both the travel direction DT and the transverse direction TR, which is generally collinear with the Z axis.

[0047] In an exemplary arrangement, the remote control device 70 is operable to wirelessly transmit a control signal representing a first type of signal, such as a travel command, to a receiver 102 on the truck 10. A travel command is also referred to herein as a "travel signal," "travel request," or "go signal." A travel request is used to initiate a request to the truck 10 to travel a predetermined amount, for example, to cause the truck 10 to generally travel only forward or slowly in a first direction of the power unit for a limited distance. The limited travel distance may be defined by an approximate travel distance, travel time, or other measurement. In one embodiment, the truck can be driven continuously as long as the duration of the travel request provided by the operator does not exceed a predetermined amount of time, for example, 20 seconds. After the operator no longer provides a travel request, or if the time for providing the travel request exceeds a predetermined period of time, the traction motor affecting the movement of the truck is no longer activated and the truck is allowed to coast to a stop. The truck 10 can be controlled to travel in a generally straight direction or along a previously determined heading.

[0048] Thus, the first type of signal received by the receiver 102 is transmitted to the controller 103. If the controller 103 determines that the travel signal is a valid travel signal and the current vehicle conditions are appropriate (explained in more detail below), the controller 103 sends a signal to the appropriate control configuration of the particular truck 10 to proceed and then stops the truck 10. For example, stopping the truck 10 can be achieved by allowing the truck 10 to coast to a stop or by initiating a braking operation to brake the truck 10 to a stop.

[0049] As an example, the controller 103 can be communicatively coupled to a traction control system, illustrated as a traction motor controller 106 of the truck 10. The traction motor controller 106 is coupled to a traction motor 107 that drives at least one driven wheel 108 of the truck 10. The controller 103 can communicate with the traction motor controller 106 to accelerate, decelerate, adjust, and / or otherwise limit the speed of the truck 10 in response to receiving a travel request from the remote control device 70. The controller 103 can also be communicatively coupled to a steering controller 112, which is coupled to a steering motor 114 that steers at least one steerable wheel 108 of the truck 10, where the steerable wheel can be different from the driven wheel. In this regard, the truck 10 can be controlled by the controller 103 to travel a desired path or maintain a desired heading in response to receiving a travel request from the remote control device 70.

[0050] As yet another illustrative example, the controller 103 may also communicate with the traction controller 106 to slow, stop, or otherwise control the speed of the truck 10 in response to receiving a travel request from the remote control device 70. Braking may be accomplished by the traction controller 106 by inducing regenerative braking or activating a mechanical brake 117 coupled to the traction motor 107, see Figure 2 Still further, the controller 103 may be communicatively coupled to other vehicle features, such as a main contactor 118, and / or other outputs 119 associated with the truck 10, where applicable, to implement desired actions in response to enabling the remote travel function.

[0051] According to various aspects of the present disclosure, controller 103 may communicate with receiver 102 and traction controller 106 to operate truck 10 under remote control in response to receiving travel commands from an associated remote control device 70 .

[0052] Correspondingly, if the truck 10 is moving in response to a command received by the remote wireless control, the controller 103 can dynamically modify, control, adjust, or otherwise affect the remote control operation, for example, by stopping the truck 10, changing the steering angle of the truck 10, or taking other actions. Thus, certain vehicle features, the state / condition of one or more vehicle features, the vehicle environment, etc. may affect the manner in which the controller 103 responds to a travel request from the remote control device 70.

[0053] The controller 103 may deny acknowledgement of a received travel request based on predetermined conditions (e.g., related to environmental or operational factors). For example, the controller 103 may ignore an otherwise valid travel request based on information obtained from one or more of the sensors 58 and 76. Illustratively, according to various aspects of the present disclosure, the controller 103 may optionally consider factors such as whether an operator is present on the truck 10 when determining whether to respond to a travel command from the remote control device 70. As described above, the truck 10 may include at least one presence sensor 58 for detecting whether an operator is present on the truck 10. In this regard, the controller 103 may also be configured to respond to a travel request to operate the truck 10 under remote control when the presence sensor(s) 58 indicate that the operator is not present on the truck 10. Thus, in such an embodiment, the truck 10 cannot be operated in response to a wireless command from the transmitter unless the operator physically leaves the truck 10. Similarly, the controller 103 may deny acknowledgement of a travel request from the transmitter 70 if the object sensor 76 detects that an object, including the operator, is adjacent to and / or in proximity to the truck 10. Thus, in the exemplary embodiment, the operator must be within a limited range of truck 10, e.g., close enough to truck 10 to be within wireless communication range (which may be limited to set a maximum distance the operator can be from truck 10). Other arrangements may alternatively be implemented.

[0054] Any other number of reasonable conditions, factors, parameters, or other considerations may also / alternatively be implemented by the controller 103 to interpret and take action in response to signals received from the transmitter.

[0055] After confirming the travel request, the controller 103 interacts, for example, directly or indirectly, with the traction motor controller 106 via a bus such as the CAN bus 110 (if used), to advance the truck 10 a limited amount. Depending on the specific implementation, the controller 103 may interact with the traction motor controller 106 and, optionally, the steering controller 112, to advance the truck 10 a predetermined distance. Alternatively, the controller 103 may interact with the traction motor controller 106 and, optionally, the steering controller 112, to advance the truck 10 for a period of time in response to detecting and maintaining actuation of a travel control on the remote control 70. As another illustrative example, the truck 10 may be configured to slow down as long as a travel control signal is received. Further, the controller 103 may be configured to "time out" and cease travel of the truck 10 based on a predetermined event, such as exceeding a predetermined time period or travel distance, regardless of whether a maintained actuation of the corresponding control on the remote control 70 is detected.

[0056] Remote control device 70 is also operable to transmit a second type of signal, such as a "stop signal," indicating that truck 10 should brake and / or otherwise come to a stop. The second type of signal may also be initiated under remote control in response to a drive command, for example, after a "drive" command is implemented, such as after truck 10 has traveled a predetermined distance, traveled for a predetermined time, etc. If controller 103 determines that the wirelessly received signal is a stop signal, controller 103 sends a signal to traction controller 106 and / or other truck components to bring truck 10 to a stop. As an alternative to a stop signal, the second type of signal may include a "coast signal" or a "controlled deceleration signal," indicating that truck 10 should coast and eventually decelerate to a stop.

[0057] The time required to bring the truck 10 to a complete stop may vary depending on, for example, the intended application, environmental conditions, the capabilities of the particular truck 10, the load on the truck 10, and other similar factors. For example, after completing a suitable creep maneuver, it may be desirable to allow the truck 10 to "coast" for a distance before coming to a stop, allowing the truck 10 to slowly come to a stop. This can be achieved by utilizing regenerative braking to slow the truck 10 to a stop. Alternatively, a braking operation may be applied after a predetermined delay time to allow the truck 10 to travel an additional predetermined distance after the stopping operation is initiated. For example, if an object is detected in the path of the truck 10 or if an immediate stop is desired after a successful creep maneuver, it may also be desirable to bring the truck 10 to a stop relatively quickly. For example, the controller may apply a predetermined torque to the braking operation. In this case, the controller 103 may instruct the traction controller 106 to brake to bring the truck 10 to a stop via regenerative braking or application of the mechanical brake 117.

[0058] Calculating vehicle driving parameter(s) used during vehicle remote control operations

[0059] As described above, the operator can stand on the platform 32 within the operator station 30 to manually operate the truck 10, that is, to operate the truck in manual mode. The operator can steer the truck 10 via the handle 52, see Figure 1B, and further, the truck 10 can be accelerated via rotation of the travel switch 54. As also described above, rotating the travel switch 54 forward and upward will cause the truck 10 to move forward at an acceleration that is proportional to the amount of rotation of the travel switch 54, for example, first the power unit. Similarly, rotating the travel switch 54 toward the rear of the truck 10 and downward will cause the truck 10 to move in the opposite direction at an acceleration that may be proportional to the amount of rotation of the travel switch 54, for example, first the forks. Rotating the travel switch 54 forward and upward while the truck 10 is moving in the first direction along the forks will cause the truck 10 to brake. Additionally, rotating the travel switch 54 toward the rear and downward while the truck 10 is moving in the first direction along the power unit will cause the truck 10 to brake. Thus, "operator manual operation of the vehicle" occurs when the operator is standing on the platform 32 within the operator's station 30 and maneuvering the truck 10 via the handle 52 and accelerating / braking the truck 10 (i.e., regenerative braking) via rotation of the travel switch 54. The operator may use a separate brake switch, for example Figure 1B The switch 41 is engaged to cause regenerative braking of the truck 10. As described above, braking may also be achieved via the mechanical brake 117.

[0060] As also described above, the controller 103 can communicate with the receiver 102 and the traction controller 106 to operate the truck 10 under remote control in response to receiving a travel command from the associated remote control device 70. The travel request is used to initiate a request to the truck 10 to travel a predetermined amount, for example, to cause the truck 10 to move forward or slow down a limited travel distance in a first travel direction, i.e., in a power unit first direction. Thus, when the operator is not physically on the truck but is walking near the truck 10, such as during a picking operation, the operator can operate the truck in a remote control mode, i.e., when the operator is outside the truck 10 and picking or collecting pick items to be loaded onto the truck 10 from a warehouse storage area, the truck 10 is operated under remote control using the remote control device 70. Operating the truck 10 in a remote control mode is also referred to herein as "semi-autonomous" operation of the truck 10.

[0061] When an operator is using the truck 10, such as during a picking operation within a warehouse, the operator typically uses the truck 10 in a manual mode and a remote control mode. Between the remote control operations, there can be a number of different manual operations of the truck 10, also referred to herein as semi-autonomous operations of the truck 10. Each such manual operation can include lifting a load, lowering a load, and / or driving the truck 10 forward or backward, as well as steering.

[0062] Previously, the vehicle controller stored predefined, fixed vehicle parameters, such as a maximum acceleration, to limit the maximum acceleration of the vehicle during operation in the remote control mode. This predefined maximum acceleration limit was sometimes too high (e.g., if the truck was loaded with a large pile of items / packages that defined an unstable load) and sometimes too low (if the truck was loaded with a small pile of items / packages that defined a stable load).

[0063] According to the present disclosure, the controller 103 monitors at least one driving parameter during the most recent manual operation of the truck 10. In some embodiments, the controller 103 may monitor one or more driving parameters prior to receiving a request from the operator to implement semi-autonomous driving operation, the one or more driving parameters corresponding to the driving behavior or traits of the operator of the truck 10. The most recent manual operation of the truck 10 may include, for example, the two most recent manual operations, the three most recent manual operations, or the four (or more) most recent manual operations. If one or more driving parameters are high, then this may correspond to the operator driving the truck 10 briskly. If one or more driving parameters are low, then this may correspond to the operator driving the truck 10 conservatively or cautiously.

[0064] Instead of using one or more predefined fixed driving parameters for vehicle control during a remote-controlled operation of the truck 10, the present disclosure calculates one or more adaptive driving parameters for use by the controller 103 during the next remote-controlled operation of the truck 10 based on one or more driving parameters monitored during two or more recent manual operations of the truck 10. Because the one or more driving parameters calculated for the next remote-controlled operation of the truck 10 are based on the operator's recent driving behavior, i.e., the one or more driving parameters monitored during the most recent manual-mode operation of the truck 10, it is believed that the present embodiment more accurately and appropriately defines the one or more driving parameters to be used during the next remote-controlled operation of the truck 10, such that the one or more driving parameters more closely match the operator's most recent driving behavior.

[0065] Figure 3 , an example control algorithm or process for controller 103 is illustrated for monitoring at least one driving parameter, or in some embodiments, first and second driving parameters, e.g., acceleration in first and second directions during a recent manual operation of truck 10, in part for use in calculating corresponding adaptive driving parameters, e.g., maximum acceleration to be used by controller 103 when truck 10 is next operated in a remote control mode. Figure 3 An example control algorithm may include monitoring the first and second driving parameters, but monitoring only the first driving parameter (eg, acceleration in the direction of travel of the truck 10 ) without monitoring the second driving parameter is also contemplated.

[0066] In step 201, the controller 103 monitors at least a first driving parameter, such as a first acceleration, corresponding to a first driving direction of the vehicle or truck 10 during a first manual operation of the vehicle. Simultaneously, the controller 103 may also monitor a second driving parameter, such as a second acceleration, corresponding to a second direction different from the first driving direction. In one embodiment, the first driving direction may be defined by the driving direction DT of the truck 10 (see FIG. 1 ), and the second direction may be defined by the transverse direction TR. Thus, the first and second directions may be substantially orthogonal to each other.

[0067] In step 203, the controller 103 monitors at least a first driving parameter, such as a first acceleration, corresponding to a first travel direction of the vehicle or truck 10 during a second manual operation of the vehicle. As described above, the controller 103 may also simultaneously monitor a second driving parameter, such as a second acceleration, corresponding to a second direction different from the first travel direction.

[0068] The controller 103 stores data associated with at least one monitored driving parameter (e.g., a first vehicle driving parameter and possibly a second vehicle driving parameter) corresponding to a first manual operation of the truck 10, as well as data associated with those driving parameters monitored during a second manual operation. Steps 201 and 203 may be repeated so that data regarding the at least one driving parameter and possibly a second driving parameter may be collected during one or more prior but most recent manual operations of the truck 10. As described below, the stored data from the most recent manual operation as well as other recent manual operations may be used to calculate a weighted average for controlling the implementation of semi-autonomous driving operation.

[0069] Because steps 201 or 203 can be repeated for other manual operations of truck 10, the term "current manual operation" can refer to the manual operation currently occurring, the term "most recent manual operation" can refer to the manual operation that occurred immediately before the manual operation that is still currently occurring, the term "previous manual operation" can refer to the manual operation that occurred before the most recent manual operation, and the term "next manual operation" can refer to the manual operation that occurs after the current manual operation. These terms refer to the manual operations of truck 10 relative to each other. For example, the first manual operation referred to in step 201 is the previous manual operation after the second manual operation is completed relative to the second manual operation referred to in step 203. However, when the first manual operation is performed, the first manual operation is also the "current manual operation" before it is stopped and the second manual operation is started. Once the "current manual operation" ends, it can be considered the "most recent manual operation", and data collected or acquired during the manual operation can be used, as described more fully below.

[0070] (Now) from Figure 3To provide a description of steps 201 and 203, Figure 4 An algorithm for an example method of performing any of steps 201 and 203 is depicted. Figure 4 In the illustrated embodiment, only a first driving parameter (eg, acceleration in a first direction) is monitored.

[0071] The operator can vary the acceleration of the truck 10 based on factors such as the curvature of the path the truck 10 is traveling, the steering angle of the truck 10, the current ground conditions (e.g., a wet / slippery floor surface or a dry / non-slip floor surface), and / or the weight and height of any load carried by the truck 10. For example, if the truck 10 is being driven with no load or with a stable load, e.g., a load having a low height, on a long, straight path, on a dry / non-slip floor surface, then the value of the first acceleration can be high. However, if the truck 10 has an unstable load, e.g., a load having a high height such that the load could be dislodged or fall from the truck 10 if the truck 10 is rapidly accelerated, then the value of the first acceleration can be low. Furthermore, if the truck 10 is turning at a sharp angle and traveling at high speed, then the value of the first acceleration can be high and the value of the second acceleration can also be high.

[0072] exist Figure 4 In the example control algorithm or process for controller 103 illustrated in FIG, a first value indicative of the acceleration of truck 10 in a first direction is calculated for a most recent manual operation of truck 10 (regardless of whether the most recent manual operation was the first manual operation referred to in step 201 or the second manual operation referred to in step 203).

[0073] In step 301, a sequence of acceleration values ​​in a first direction, defined by the driving direction DT of the truck 10 (i.e., the direction the truck 10 is moving, either in the forward or power unit first direction or in the reverse or fork first direction), are collected from the accelerometer 1103 during the most recent manual operation of the vehicle and stored in memory by the controller 103. Rotating the travel switch 54 forward and upward will cause the truck 10 to move forward with a positive acceleration proportional to the amount of rotation of the travel switch 54 in the power unit first direction, e.g., the power unit first. Similarly, rotating the travel switch 54 toward the rear of the truck 10 and downward will cause the truck 10 to move in the reverse direction with a positive acceleration proportional to the amount of rotation of the travel switch 54 in the fork first direction, e.g., the fork first. When the truck 10 accelerates in either the power unit first direction or the fork first direction (both of which are considered to be the first direction defined by the driving direction DT of the truck 10), the accelerometer 1103 generates a sequence of positive acceleration values, which are stored in memory by the controller 103. Rotating the travel switch 54 forward and upward while the truck 10 is moving in the first direction of the forks will cause the truck 10 to slow down or brake. Additionally, rotating the travel switch 54 backward and downward while the truck 10 is moving in the first direction of the power unit will cause the truck 10 to slow down or brake. According to the first embodiment of the present disclosure, negative acceleration values, such as those occurring during braking, are not collected for use in calculating the first value indicating the acceleration of the truck 10 in the first direction during the most recent manual operation of the vehicle.

[0074] While rotating the limit switch 54 forward and upward will cause the truck 10 to move forward in the power unit first direction with positive acceleration (increasing speed), i.e., power unit first, the accelerometer can determine that such movement includes positive acceleration. The accelerometer can also determine that braking (decreasing speed) while the truck 10 is traveling in the power unit first direction includes deceleration or negative acceleration. Additionally, when rotating the limit switch 54 toward the rear and downward will cause the truck 10 to move in the opposite direction in the fork first direction with positive acceleration (increasing speed), e.g., fork first, the accelerometer can determine that such movement in which speed is increasing in the fork first direction includes negative acceleration. The accelerometer can also determine that braking (decreasing speed) while the truck 10 is traveling in the fork first direction includes positive acceleration. However, for the purposes of discussing herein the control algorithm for calculating the maximum acceleration to be used during the next semi-autonomous driving operation, the acceleration and deceleration of the truck 10 during movement in the power unit first direction and the fork first direction will be defined as follows: rotation of the travel switch 54 forward and upward causing the truck 10 to move forward (e.g., power unit first) is defined as a positive acceleration in the power unit first direction (speed is increasing); rotation of the travel switch 54 toward the rear and downward causing the truck 10 to move in the reverse direction (e.g., fork first) is defined as a positive acceleration in the fork first direction (speed is increasing); rotation of the travel switch 54 forward and upward or actuation of the brake switch 41 when the truck 10 is moving in the fork first direction causing the truck 10 to decelerate or brake (speed is decreasing) is defined as a negative acceleration or deceleration; and rotation of the travel switch 54 backward and downward or actuation of the brake switch 41 when the truck 10 is moving in the power unit first direction causing the truck 10 to decelerate or brake (speed is decreasing) is defined as a negative acceleration or deceleration.

[0075] As described above, according to the first embodiment of the present disclosure, negative acceleration values, such as those occurring during braking in the power unit first direction or the fork first direction, are not collected for use in calculating the first value indicating the acceleration of the truck 10 in the first direction during the most recent manual operation of the vehicle. However, according to the second embodiment of the present disclosure, both positive acceleration values ​​(where the speed of the truck is increasing in either the power unit first direction or the fork first direction) and negative acceleration values ​​(where the speed of the truck is decreasing in either the power unit first direction or the fork first direction) are collected and used to calculate the first value indicating the acceleration of the truck 10 in the first direction during the most recent manual operation of the vehicle. In the second embodiment in which negative acceleration values ​​are collected, the absolute values ​​of the negative acceleration values ​​are used in the described equations and calculations described below. Therefore, while some embodiments of the present disclosure may ignore any negative acceleration data, other embodiments may take such data into account by using the absolute values ​​of the negative acceleration data in the described equations and calculations.

[0076] In step 303, the acceleration values ​​in the first direction collected during the most recent manual operation of the truck 10 are filtered using a weighted average equation to reduce the weight of the largest outliers and achieve smoothing. Example Equation 1 listed below can be used to filter the collected acceleration values ​​in the first direction to calculate a weighted average based on the acceleration values ​​in the first direction collected from the most recent manual operation of the truck 10. Therefore, each weighted average can be considered as a corresponding processed value associated with the acceleration values ​​in the first direction collected during the most recent manual operation of the truck 10 (i.e., a corresponding processed value associated with the first monitored vehicle driving parameter).

[0077] Equation 1:

[0078] wa x-(i+1) = a processed value comprising a weighted average calculated in a first direction (eg, "x"); where i = 1...(n-1) and n is the individual collected acceleration values ​​a x_j The total number of subsets into which it was grouped;

[0079] wa x-i ;where i=1...n; wa x-i = a processed value comprising the arithmetic mean of the first three "start" acceleration values ​​in the first direction during the first calculation and the most recent weighted mean thereafter;

[0080] g s = weighting factors, where s = 1…m+1, where m is the number of members in each subset;

[0081] g1=wa x-i In the embodiment shown, g1=3, but it can be any value;

[0082] g2, g3, g4 = additional weighting factors = 1, but can be any value and are usually smaller than g1;

[0083] a x_[(i*m)+1] , a x_[(i*m)+2] , a x_[(i*m)+3] , where i=1...(n-1); a x_[(i*m)+1] , a x_[(i*m)+2] , a x_[(i*m)+3] = three adjacent individual acceleration values ​​in a first direction, defining a subset collected during the most recent manual operation of the truck 10. The subset may include more or less than three acceleration values. The first three collected acceleration values ​​(a x_1 、a x_2 and a x_3 ) also constitute the first subset.

[0084] The first "starting" acceleration value in the first direction may include fewer than three or more than three values, and the number of members in each subset "m" may likewise include fewer than three or more than three members.

[0085] For illustrative purposes, a sample calculation will now be provided based on unrealistic sample values ​​simulating acceleration values ​​collected in a first direction and will be presented in Figure 5 The acceleration values ​​listed in Table 1 are all positive. However, as described above, negative acceleration values ​​can also be collected and used. As described above, when negative acceleration values ​​are collected, the absolute value of the negative acceleration value is used in the equations described and in the calculations listed below.

[0086]

[0087]

[0088]

[0089] based on Figure 5 The remaining weighted averages of the sample values ​​listed in Table 1 are calculated in a similar manner. The results are listed in Figure 6 Table 2.

[0090] Therefore, for Equation 1, the value a x_[(i*m)+1] , a x_[(i*m)+2] , and a x_[(i*m)+3] Used to calculate the weighted average wa x-(i+1) .according to Figure 5 For example, “i” can range from 1 to 9, but for equation 1, “i” ranges from 1 to 8. Therefore, 27 individually collected acceleration values ​​(i.e., a x_j , where "j" ranges from 1 to k, where Figure 5 The values ​​of k=27 in the table (k=27) can be arranged into 9 different subsets, each with 3 elements. Except for the first subset (which, as described above, includes the arithmetic mean of the first three "start" acceleration values ​​in the first direction), for each of the subsequent 8 subsets, a weighted average is calculated according to Equation 1. The example initial arithmetic mean and the example 8 weighted averages are shown in Figure 6 One of ordinary skill will readily recognize that the subset size of 3 values ​​is merely an example, and that using 9 subsets is also an example amount.

[0091] exist Figure 4 In step 305, the maximum acceleration in the first direction defined by the direction of travel DT of the truck 10 is determined for the most recent manual operation using the example equation 2 listed below:

[0092] Equation 2: a x-wa-max= Maximum acceleration in the first direction = max(wa x-i ) = the maximum of the processed values, or in other words, the calculated initial arithmetic and weighted average (wa x-i ) is the maximum value of .

[0093] based on Figure 6 The results in Table 2 show that max(wa x-i )=a x-8 =3.82.

[0094] Note that a x-wa-max The initial arithmetic and weighted average (wa x-i For example, the average value (wa x-i It is also envisaged that a predetermined number of initial arithmetic and weighted averages (wa x-i ), for example, 25 averages. It is further contemplated that all initial arithmetic and weighted averages (wa x-i In the example shown, the initial arithmetic and weighted average values ​​(wa x-i ). However, in choosing max(a x-wa-i )=the calculated initial arithmetic and weighted average (Wa x-i ) (which defines a x-wa-max = maximum acceleration in the first direction), less than 9 or more than 9 initial arithmetic and weighted averages (wa x-i ) value. The maximum acceleration in the first direction (a x-wa-max ) defines a first value indicating the acceleration of the vehicle in a first direction during a most recent manual operation of the vehicle. x-wa-max The initial arithmetic and weighted average of the maximum acceleration on x-i ) in the set, but rather assumes that the initial arithmetic and weighted average (wa x-i ) can be selected as the maximum acceleration a in the first direction x-wa-max . Further suppose that the initial arithmetic and weighted average (wa x-i ) can be averaged to determine the maximum acceleration a in the first direction (during the most recent manual operation) x-wa-max .

[0095] As described above, the second driving parameter may also be monitored in either of steps 201 and 203. An example control algorithm or process of the controller 103 is Figure 7 1 , for calculating a second value indicative of the acceleration of the truck 10 in a second direction during a recent manual operation of the truck 10. At step 401, a sequence of acceleration values ​​in a second direction, defined by the transverse direction TR (see FIG1 ), is collected from the accelerometer 1103 and stored in a memory by the controller 103.

[0096] In step 403, the collected acceleration values ​​in the second direction collected during the most recent manual operation of the truck 10 are filtered using a weighted average equation to reduce the weight of the largest outliers and achieve smoothing. Example Equation 3 listed below can be used to filter the acceleration values ​​collected in the second direction during the most recent manual operation of the truck 10. Therefore, each weighted average value can be considered as a corresponding processed value associated with the acceleration values ​​in the second direction collected during the most recent manual operation of the truck 10 (i.e., a corresponding processed value associated with the second monitored vehicle driving parameter).

[0097] Equation 3:

[0098] wa y-(i+1) = a processed value comprising a weighted average calculated in a second direction (eg, "y"); where i = 1...(n-1) and n is the individual collected acceleration values ​​a y_j The total number of subsets into which it was grouped;

[0099] wa y-i ;where i=1...n; wa y-i = a processed value comprising the arithmetic mean of the first three “start” acceleration values ​​in the second direction at the time of the first calculation and thereafter the most recently calculated weighted mean during the most recent manual operation;

[0100] g s = weighting factors, where s = 1…m+1, where m is the number of members in each subset;

[0101] g1=wa y-i In the embodiment shown, g1=3, but it can be any value;

[0102] g2, g3, g4 = additional weighting factors = 1, but can be other values;

[0103] a y_[(i*m)+1] , a y_[(i*m)+2] , a y_[(i*m)+3] ;where i=1...(n-1); a y_[(i*m)+1] , a y_[(i*m)+2] , a y_[(i*m)+3]= three adjacent individual acceleration values ​​in the second direction, defining a subset collected during the most recent manual operation of the truck 10. The subset may include more or less than three acceleration values. The first three collected acceleration values ​​(a y_1 、a y_2 and a y_3 ) also constitute the first subset.

[0104] The first "starting" acceleration value in the second direction may include fewer than three or more than three values, and the number of members in each subset "m" may likewise include fewer than three or more than three members.

[0105] For illustrative purposes, a sample calculation will now be provided based on unrealistic sample values ​​that simulate acceleration values ​​collected in the second direction and will be presented in Figure 8 are listed in Table 3.

[0106]

[0107]

[0108] based on Figure 8 The remaining weighted averages of the sample values ​​listed in Table 3 are calculated in a similar manner. The results are listed in Figure 9 Table 4.

[0109] exist Figure 7 In step 405, the maximum acceleration in the second direction defined by the transverse direction TR of the truck 10 is determined (for the most recent manual operation) using Equation 4 as listed below:

[0110] Equation 4: a y-wa-max = Maximum acceleration in the second direction = max(wa y-i ) = the maximum of the processed values, or in other words, the calculated initial arithmetic and weighted average (wa y-i ) is the maximum value of .

[0111] based on Figure 9 The results in Table 4 show that max(wa y-i )=wa y-2 =0.55.

[0112] Note that a y-wa-max It can be selected from the initial arithmetic mean or any number of weighted averages (weighted averages) calculated. y-(i+1) For example, an initial arithmetic and weighted average (wa y-i It is also envisaged that a predetermined number of initial arithmetic and weighted averages (wa y-i), for example, 25 averages. It is further contemplated that all initial arithmetic and weighted averages (wa y-i In the example shown, the initial arithmetic and weighted average values ​​(wa y-i ). However, in selecting max(wa y-i ) = calculated initial arithmetic and weighted average (wa y-i ) (which defines a y-wa-max = maximum acceleration in the second direction), less than 3 or more than 3 initial arithmetic and weighted averages (wa y-i ) value. The maximum acceleration of the vehicle in the second direction (a y-wa-max ) defines a second value indicating the acceleration of the vehicle in a second direction during a most recent manual operation of the vehicle.

[0113] By repeating steps 201 and 203 for a plurality of manual operations, respective first and second values ​​a indicating the acceleration of the vehicle in the first and second directions may be calculated for each of the plurality of manual operations. x-wa-max and a y-wa-max For illustration purposes, sample calculations will now be provided based on non-real sample values ​​that simulate calculated maximum acceleration values ​​in the first and second directions, which are first and second values ​​indicating the acceleration of the vehicle in the first and second directions and are Figure 10 and Figure 11 Listed in.

[0114] exist Figure 10 There are a corresponding to the four most recent manual operations of the truck 10 x-wa-max As mentioned above, a x-wa-max The maximum value of all different treatment values ​​calculated during a particular manual operation can be included. The label a in the leftmost column x-wa-max-1 The label a in the second column is the maximum acceleration value in the first direction calculated during the most recent manual operation of the truck 10. x-wa-max-2 is the maximum acceleration value in the first direction calculated during the immediately preceding manual operation of the truck 10. The label a in the third column x-wa-max-3 is the maximum acceleration value in the first direction calculated during the second-most-previous manual operation of the truck 10. The label a in the rightmost column x-wa-max-4is the maximum acceleration value in the first direction calculated during the third-most-previous manual operation of the truck 10. Therefore, when the current manual operation ends and thus becomes the most recent manual operation, the value a calculated for the most recent manual operation is x-wa-max Placed Figure 10 , and these 4 values ​​are shifted right, removing the oldest value from the table. Figure 10 The example table of includes 4 elements, but one of ordinary skill will recognize that embodiments of the present disclosure contemplate more or less than 4 such values, and may, for example, include all manual operations that occur before the next semi-automatic operation.

[0115] exist Figure 11 There are a corresponding to the three most recent manual operations of the truck 10 y-wa-max The label a in the leftmost column is y-wa-max-1 The label a in the second column is the maximum acceleration value in the second direction calculated during the most recent manual operation of the truck 10. y-wa-max-2 is the maximum acceleration value in the second direction calculated during the immediately preceding manual operation of the truck 10. The label a in the third column x-wa-max-3 is the maximum acceleration value in the second direction calculated during the second previous manual operation of the truck 10. Therefore, when the current manual operation ends and thus becomes the most recent manual operation, the value a calculated for the most recent manual operation y-wa-max Placed Figure 11 , and these 3 values ​​are shifted right, removing the oldest value from the table. Figure 11 The example table of includes 3 elements, but one of ordinary skill will recognize that embodiments of the present disclosure contemplate more or less than 3 such values, and may, for example, include all manual operations that occur before the next semi-automatic operation.

[0116] Now return to Figure 3 In step 205, the controller receives a request to implement semi-autonomous driving operation of the truck 10. The receipt of this request may occur after steps 201 and 203 have occurred, so that the controller has already calculated and stored data values ​​related to acceleration, such as Figure 10 and Figure 11 Those example values ​​in the table.

[0117] In step 207, a weighted average is calculated using the monitored at least one driving parameter from a plurality of different manual operations of the truck 10. Figure 3 The example process explicitly refers to only the first and second manual operations occurring, but Figure 10The example data in the table refers to the four most recent manual operations of the truck 10. Therefore, in step 207, for each of the four most recent manual operations of the vehicle, the maximum acceleration (a) of the vehicle in the first direction is calculated. x-wa-max ) corresponding to Figure 10 The four example processed values ​​in the table can be used to calculate the total weighted average value a of the maximum acceleration in the first direction for the four most recently manually operated vehicles according to Equation 5 as shown below: x-next_QPR :

[0118]

[0119] in

[0120] g s = weighting factor, where s = 1...4 (in this example); and

[0121] g1, g2, g3, g4 = corresponding maximum acceleration values ​​applied to the vehicle in the first direction (a x-wa-max ) (each also a weighted average). As an example, g1 may be equal to 4, g2 may be equal to 2, g3 may be equal to 1.5, and g4 may be equal to 1. Each of g1, g2, g3, and g4 may be equal to any value. The maximum acceleration value a x-wa-max-1 ;a x-wa-max-2 ;a x-wa-max-3 ;a x-wa-max-4 The total weighted average value a of the maximum acceleration of the vehicle in the first direction is multiplied by their corresponding weighting factors g1, g2, g3, g4. x-next_QPR The first, second, third and fourth components of .

[0122] Using the example values ​​and weighting factors above, the calculated total weighted average of the vehicle's maximum acceleration in the first direction for the four most recent manual maneuvers is:

[0123]

[0124] Different from step 207, Figure 11 The three values ​​in the table can be used to calculate different total weighted average values ​​a of the maximum acceleration of the vehicle in the second direction for the three most recent manual operations according to Equation 6 as shown below: y-next_QPR :

[0125]

[0126] in

[0127] g s = weighting factors, where s = 1…3 (in this example); and

[0128] g1, g2, g3 = corresponding maximum acceleration values ​​applied to the vehicle in the second direction (a y-wa-max ) (each also a weighted average). As an example, g1 may be equal to 4, g2 may be equal to 2, and g3 may be equal to 1. Each of g1, g2, and g3 may be equal to any value. The maximum acceleration value a y-wa-max-1 ;a y-wa-max-2 ;a y-wa-max-3 The total weighted average value a of the maximum acceleration of the vehicle in the second direction multiplied by their corresponding weighting factors g1, g2, g3 is referred to herein as the total weighted average value a of the maximum acceleration of the vehicle in the second direction y-next_QPR The first, second and third components of .

[0129] Using the example values ​​and weighting factors above, the calculated weighted average of the vehicle's maximum acceleration in the second direction for the three most recent manual maneuvers is:

[0130]

[0131] While the above weighting factors are provided as examples only, weighting more recent values ​​more heavily than less recent values ​​tends to allow recent manual driving behavior to have a greater influence on each of the calculated weighted averages. Figure 10 and Figure 11 In the example embodiment described above, the number of recent manual operations considered when calculating the weighted average of the vehicle's maximum acceleration in the first direction (i.e., 4) is different from the number of recent manual operations considered when calculating the weighted average of the vehicle's maximum acceleration in the second direction (i.e., 3). It is also contemplated that these two numbers may be the same.

[0132] Finally, in Figure 3 In step 209, the controller 103 may calculate the weighted average value a based at least in part on the weighted average value a. x-next_QPR To control the implementation of the next semi-autonomous driving operation.

[0133] exist Figure 12 An example control algorithm or process for the controller 103 is illustrated in FIG. 1 for determining a vehicle's maximum acceleration in a first direction based at least in part on a calculated total weighted average value a of the vehicle's maximum acceleration in a first direction. x-next_QPR to calculate the maximum acceleration to be used during the next semi-autonomous driving maneuver. Figure 12 In the example process, in order to achieve the maximum acceleration, the weighted average a is calculated. x-next-QPR Another weighted average value a based on the maximum acceleration of the vehicle in the second direction y-next_QPRThese weighted averages represent first and second weighted averages indicating the acceleration of the truck 10 in the first direction and the second direction during the most recent manual operation of the truck 10. As described above, the first weighted average indicating the acceleration of the truck 10 in the first direction is determined by the weighted average a x-next_QPR is defined, and the second weighted average value indicating the acceleration of the truck 10 in the second direction is given by the weighted average value a y-next_QPR Definition. During operation of the truck 10, the operator may drive the truck 10 quickly along a generally straight path, but slowly during a turn. To account for the operator driving the truck 10 slowly during a turn, in step 501, the controller 103 calculates the weighted average value (a) in the second direction. y-next_QPR ) is compared with the empirically determined range listed in the lookup table stored in the memory to determine the weighted average value (a x-next_QPR ) is appropriate.

[0134] As explained in detail below, when determining the maximum acceleration for the next semi-autonomous driving operation, the weighted average value in the second direction (a y-next_QPR ) can be used to correct or adjust the calculated weighted average value in the first direction (a x-next-QPR The weighted average value in the second direction (a y-next_QPR ) may indicate the operator's assessment of the stability of the truck 10 and its current load. If the weighted average value in the second direction is greater than the first empirically derived value or falls within the empirically derived "high acceleration" range, then this may indicate to the operator that the load is relatively stable and the maximum acceleration for the next semi-autonomous driving maneuver may be increased. However, if the weighted average value in the second direction is less than the second empirically derived value or falls within the empirically defined "low acceleration" range, then this may indicate to the operator that the load is likely unstable, even though the calculated weighted average value in the first direction is relatively high. Therefore, in this second case, the maximum acceleration for the next semi-autonomous driving maneuver may be reduced. If the weighted average value in the second direction is between the first and second empirically derived values ​​or within the empirically defined intermediate range, then no correction or adjustment is made to the maximum acceleration for the next semi-autonomous driving maneuver. The high, low, and intermediate ranges (or empirically derived first and second values) can be determined empirically for a particular vehicle in a controlled environment in which the vehicle is operated at various maximum accelerations in a first direction and a second direction, and different values ​​for the various high, low, and intermediate ranges are created, and using a weighted average in the second direction, a correction factor is determined and used to adjust the weighted average in the first direction. Preferred high, low, and intermediate ranges are selected that allow for optimal acceleration in the first direction while allowing the truck to carry and support the load in a stable manner.

[0135] Figure 13 An exemplary simulation lookup table based on non-real values ​​is listed in FIG. 1 , which contains the weighted average values ​​(a) for the second direction. y-next_QPR ). If the weighted average acceleration in the second direction falls within Figure 13 If the weighted average in the second direction falls within the high acceleration or low acceleration range depicted in the lookup table of FIG. , the corresponding correction factor is used to determine the maximum acceleration to be used during the next semi-autonomous driving operation of the truck 10. Figure 13 If the maximum acceleration is within the intermediate acceleration range (or intermediate range) depicted in the lookup table of , then the correction factor corresponding to the weighted average in the second direction is not used when determining the maximum acceleration to be used during the next semi-autonomous driving operation of the truck 10.

[0136] In the example discussed above, the weighted average in the second direction (a y-next_QPR ) = 0.51. This value falls within the high acceleration range corresponding to a correction factor of +10%.

[0137] In step 503, the maximum acceleration to be used during the next semi-autonomous driving maneuver (which may also be referred to as the "semi-autonomous driving maneuver maximum acceleration") is calculated using example Equation 7:

[0138] Equation 7: max.acc = a x-next_QPR *(1+corr x +corr y )

[0139] where max.acc = the maximum acceleration in the first direction to be used during the next semi-autonomous driving maneuver;

[0140] corr x = safety margin, which can be equal to any value. In the embodiment shown, corr x = -5% (may include negative values ​​as in the illustrated embodiment to reduce max.acc to provide a safety margin);

[0141] corr y = Figure 13 The correction factor in the lookup table is calculated based on the weighted average value (a y-next_QPR ).

[0142] A sample calculation of max.acc based on the sample values ​​discussed above will now be provided.

[0143] max.acc=a x-next_QPR *(1+corr x +corr y )=3.19*(1–0.05+0.1)=3.35

[0144] Therefore, in this example, the controller 103 communicates with the traction motor controller 106 to limit the maximum positive acceleration of the truck 10 in the first direction (increasing speed) to 3.35 m / s during the next semi-autonomous or remotely controlled operation. 2 .

[0145] It is also envisioned that Figure 13 , which has an intermediate range and an outlier range. For example, determining a "high acceleration" in the second direction may result in applying a correction factor corr y = 0 to avoid reducing max.acc. When determining "medium acceleration", a correction factor corr can be applied y = -0.05 (ie 5%) to reduce max.acc to provide a safety margin, and when determining "low acceleration", a correction factor corr may be applied y =-0.10 (i.e., 10%) to reduce max.acc to provide a safety margin. Furthermore, the determination of the acceleration in the second direction is not limited to only 3 different ranges, and alternative embodiments of the present disclosure also contemplate more than just 3 levels of ranges, such as, for example, 4, 5, or more ranges, each having a corresponding predetermined correction factor value associated therewith.

[0146] It is also contemplated that the controller 103 may calculate a first value indicative of only the deceleration of the vehicle in the first direction during one or more most recent manual operations of the vehicle using Equations 1 and 2 listed above, wherein the absolute value of each deceleration value collected from the one or more most recent manual operations of the vehicle is used to calculate the first value using Equations 1 and 2. Deceleration values ​​corresponding to emergency braking, which may have very high magnitudes, are disregarded in calculating the first value indicative of the vehicle's deceleration.

[0147] In the event that the truck 10 does not have an accelerometer, the acceleration values ​​in the first direction and the second direction can be calculated in an alternative manner. For example, the acceleration in the direction of travel DT or the first direction can be determined using a speed sensor, which can be provided on the traction motor controller. The controller 103 can differentiate the speed or speed value to calculate the acceleration value. The acceleration can also be derived from the angular position of the travel switch 54 relative to the home position, as described above, the travel switch 54 controls the acceleration / braking of the truck 10. The angular position of the travel switch 54 is used as an input to a lookup table, from which the truck acceleration is selected. The lookup table corresponds specific travel switch angular position values ​​to specific acceleration values. The maximum speed value can also be provided by the lookup table based on the travel switch angular position.

[0148] The acceleration in the transverse direction TR or the second direction can be determined using the following equation: y =v 2 / r

[0149] where v = truck speed; and

[0150] r = radius of the curve through which the truck is moving;

[0151] The radius r can be calculated using the following equation:

[0152] r = wheelbase dimension / sinα

[0153] Wherein the wheelbase dimension is a fixed value and is equal to the distance from the front wheels to the rear wheels of the truck 10; and

[0154] The steering angle α is usually known by the controller 103 because it is the steering wheel angle.

[0155] In the above description, reference is made to individual or separate manual vehicle driving operations. During each manual operation, one or more vehicle driving parameters are monitored for each manual operation. For example, as described above, a plurality of acceleration values ​​of the vehicle traveling in a first direction may be monitored and used to calculate a weighted average for each such manual operation. The respective weighted average for each manual operation is based on the monitored driving parameters occurring during that particular manual operation. These respective different weighted averages may then be used to calculate an overall weighted average for the vehicle traveling in the first direction (i.e., a x-next-QPR ).

[0156] Figure 5 The table represents the driving parameters monitored during a single manual operation. Thus, the controller 103 defines the start and end of each manual operation so that data associated with each manual operation can be kept separate from data belonging to different manual operations. A particular manual operation can be considered to have begun when an operator is on the truck 10, such as indicated by the presence sensor 58, and moves the truck 10 at at least the minimum speed. Alternatively, a particular manual operation can be considered to have begun when a driving signal is generated via the travel switch 54 rather than via the remote control device 70. It is further contemplated that a particular manual operation can be considered to have begun when the operator is located outside the operator's station 30 and moves the truck by activating the driving control switch 140 located near the top of the second end section 14B of the power unit 14 of the truck 10 (see Figure 1AA particular manual operation may be considered completed when the truck 10 remains stationary for at least a predetermined period of time. Alternatively, a particular manual operation may be considered completed when the truck 10 stops and the operator leaves the truck. Alternatively, a particular manual operation may be considered completed when the operator initiates a semi-autonomous driving operation. Furthermore, a manual operation may be considered completed when the operator leaves the platform of the truck 10 even while the truck 10 is still moving.

[0157] U.S. Provisional Patent Application No. 62 / 892,213, filed on August 27, 2019, and entitled “Adaptive Acceleration for Materials Handling Vehicle,” is incorporated herein by reference in its entirety, and U.S. Serial No. 16 / 943,567, filed on July 30, 2020, is also incorporated by reference in its entirety.

[0158] Having thus described the application in detail and by reference to embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the invention as defined in the appended claims.

Claims

1. A method for operating a material handling vehicle, comprising: monitoring, by the controller, a first vehicle driving parameter during a first manual operation of the vehicle by the operator; monitoring, by the controller, a first vehicle driving parameter during a second manual operation of the vehicle by the operator; receiving, by the controller, a request to implement a semi-automated driving operation after a first manual operation of the vehicle and a second manual operation of the vehicle; calculating, by the controller, a first weighted average based on a first vehicle driving parameter monitored during a first manual operation of the vehicle and a first vehicle driving parameter monitored during a second manual operation of the vehicle; as well as Based at least in part on the calculated first weighted average, implementation of the semi-autonomous driving operation is controlled by the controller.

2. The method of claim 1 , wherein calculating the first weighted average comprises: calculating a first component of a first weighted average by applying a first weight value to a first processed value associated with a first vehicle driving parameter monitored during a first manual operation of the vehicle; calculating a second component of the first weighted average by applying a second weight value to a second processed value associated with the first vehicle driving parameter monitored during a second manual operation of the vehicle; as well as A first weighted average is calculated based on the calculated first component and second component. The method of claim 2 , wherein the first weight value is different from the second weight value.

4. The method of any one of claims 2 or 3, wherein the second weight value is greater than the first weight value.

5. The method of claim 1 , wherein the second manual operation of the vehicle occurs closer in time to receiving the request to enable the semi-autonomous driving operation than the first manual operation. 6 . The method of claim 1 , wherein the first vehicle driving parameter monitored during the first manual operation and the second manual operation of the vehicle corresponds to a first driving direction of the vehicle.

7. The method of claim 6, comprising: A second vehicle driving parameter corresponding to a second direction different from the first direction of travel is simultaneously monitored by the controller using the first vehicle driving parameter during first and second manual operations of the vehicle by the operator.

8. The method of claim 7, comprising: A second weighted average is calculated by the controller based on a second vehicle driving parameter monitored during a first manual operation of the vehicle and a second vehicle driving parameter monitored during a second manual operation of the vehicle. 9 . The method of claim 8 , wherein the first vehicle driving parameter comprises acceleration in a first direction and the second vehicle driving parameter comprises acceleration in a second direction.

10. The method of any one of claims 7 to 9, wherein the first direction and the second direction are substantially orthogonal to each other.

11. The method of claim 8, further comprising: When the calculated second weighted average falls outside the predefined middle range, the calculated first weighted average is modified based on the calculated second weighted average.

12. The method of claim 11, further comprising: Based on the modified first weighted average value, the controller controls the implementation of the semi-automatic driving operation.

13. The method of claim 1, wherein controlling the implementation of the semi-autonomous driving operation includes limiting the maximum acceleration of the vehicle.

14. The method of claim 1, wherein: The semi-automatic driving operation of the vehicle occurs between the first manual operation and the second manual operation.

15. The method of claim 1, wherein: Calculating the first weighted average includes: calculating a first component of a first weighted average by applying a first weight value to a first average associated with a first vehicle driving parameter monitored during a first manual operation of the vehicle; calculating a second component of the first weighted average by applying a second weight value to a second average associated with the first vehicle driving parameter monitored during a second manual operation of the vehicle; and A first weighted average is calculated based on the calculated first component and second component.

16. A system for operating a material handling vehicle, comprising: a memory for storing executable instructions; as well as a processor in communication with the memory, wherein execution of the executable instructions by the processor causes the processor to: monitoring a first vehicle driving parameter during a first manual operation of the vehicle by an operator; monitoring a first vehicle driving parameter during a second manual operation of the vehicle by the operator; receiving a request to enable semi-autonomous driving operation after a first manual operation of the vehicle and a second manual operation of the vehicle; calculating a first weighted average based on a first vehicle driving parameter monitored during a first manual operation of the vehicle and a first vehicle driving parameter monitored during a second manual operation of the vehicle; as well as Implementation of the semi-autonomous driving operation is controlled based at least in part on the calculated first weighted average.

17. The system of claim 16, wherein calculating the first weighted average comprises: calculating a first component of a first weighted average by applying a first weight value to a first processed value associated with a first vehicle driving parameter monitored during a first manual operation of the vehicle; calculating a second component of the first weighted average by applying a second weight value to a second processed value associated with the first vehicle driving parameter monitored during a second manual operation of the vehicle; as well as A first weighted average is calculated based on the calculated first component and second component. The system of claim 17 , wherein the first weight value is different from the second weight value.

19. The system of claim 17 or 18, wherein the second weight value is greater than the first weight value.

20. The system of claim 16, wherein the second manual operation of the vehicle occurs closer in time to receiving the request to enable the semi-autonomous driving operation than the first manual operation.

21. The system of claim 16, wherein the first vehicle driving parameter monitored during the first manual operation and the second manual operation of the vehicle corresponds to a first direction of travel of the vehicle.

22. The system of claim 21 , wherein execution of the executable instructions by the processor causes the processor to: A second vehicle driving parameter corresponding to a second direction different from the first direction of travel during the first and second manual operations of the vehicle by the operator is simultaneously monitored using the first vehicle driving parameter.

23. The system of claim 22, wherein execution of the executable instructions by the processor causes the processor to: A second weighted average is calculated based on a second vehicle driving parameter monitored during a first manual operation of the vehicle and a second vehicle driving parameter monitored during a second manual operation of the vehicle.

24. The system of claim 23, wherein the first vehicle driving parameter comprises acceleration in a first direction and the second vehicle driving parameter comprises acceleration in a second direction.

25. The system of any of claims 22-24, wherein the first direction and the second direction are substantially orthogonal to each other.

26. The system of claim 23, wherein execution of the executable instructions by the processor causes the processor to: When the calculated second weighted average falls outside the predefined middle range, the calculated first weighted average is modified based on the calculated second weighted average.

27. The system of claim 26, wherein execution of the executable instructions by the processor causes the processor to: Based on the modified first weighted average value, implementation of the semi-automatic driving operation is controlled.

28. The system of claim 16, wherein controlling the implementation of the semi-autonomous driving operation includes limiting the maximum acceleration of the vehicle.

Images