WO2016172729A1 - Système, appareil et procédé permettant de commander un véhicule - Google Patents

Système, appareil et procédé permettant de commander un véhicule Download PDF

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Publication number
WO2016172729A1
WO2016172729A1 PCT/US2016/029256 US2016029256W WO2016172729A1 WO 2016172729 A1 WO2016172729 A1 WO 2016172729A1 US 2016029256 W US2016029256 W US 2016029256W WO 2016172729 A1 WO2016172729 A1 WO 2016172729A1
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Prior art keywords
path
curvature
autonomous vehicle
clothoid
connection point
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PCT/US2016/029256
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English (en)
Inventor
Matthew D. Berkemeier
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Autonomous Solutions, Inc.
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Publication date
Application filed by Autonomous Solutions, Inc. filed Critical Autonomous Solutions, Inc.
Priority to BR112017022955-2A priority Critical patent/BR112017022955B1/pt
Priority to AU2016253152A priority patent/AU2016253152B2/en
Publication of WO2016172729A1 publication Critical patent/WO2016172729A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
    • A01B69/00—Steering of agricultural machines or implements; Guiding agricultural machines or implements on a desired track
    • A01B69/007—Steering or guiding of agricultural vehicles, e.g. steering of the tractor to keep the plough in the furrow
    • A01B69/008—Steering or guiding of agricultural vehicles, e.g. steering of the tractor to keep the plough in the furrow automatic
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory

Definitions

  • the present disclosure relates to systems and methods for controlling autonomous vehicles. More specifically, the present disclosure relates to systems and methods that are configured to create and execute optimal drivable path plans for autonomous vehicles based on clothoid segments, as well as methods and controllers configured to control autonomous vehicles to follow paths that include arcs, straight lines, and clothoid segments.
  • Vehicles such as automobiles, off-road vehicles, agricultural tractors, or self- propelled agricultural im plements, may be used in a variety of tasks (e.g., to transport people or goods from one location to another, to tow agricultural implements, to harvest, plow, cultivate, spray, etc.).
  • tasks e.g., to transport people or goods from one location to another, to tow agricultural implements, to harvest, plow, cultivate, spray, etc.
  • vehicles are manually operated by an operator. That is, the steering and speed of a vehicle are controlled by an operator driving the vehicle. Unfortunately, the operator may not drive the vehicle along an efficient path from one location to another location as compared to autonomously controlled vehicles.
  • Clothoid curves have a continuous rate of curvature as a function of path length.
  • a clothoid is a curve where the curvature varies linearly with curve length. Paths generated with these types of curves are "drivable" in that no instantaneous changes in curvature rate are required.
  • a clothoid path may parameterized by six quantities including initial position, initial heading, initial curvature, rate of curvature (with respect to path length) and path length
  • the various systems and methods of the present disclosure have been developed in response to the present state of the art, and in particular, in response to the problems and needs in the art that have not yet been fully solved by currently available technology.
  • the systems and methods of the present disclosure may provide optimal transitions between path segments that minimize off-path normal error and/or optimal transitions between path segments that minimize lateral acceleration and achieve smoother transitions between path segments, thus reducing wear and tear on autonomous vehicles and saving maintenance costs.
  • the present disclosure may also provide a controller and method for achieving optimal transitions between path segments that minimize off-path normal error to better track desired path plans.
  • the algorithms and controller described herein may be used with any system that controls a vehicle or object on a path.
  • controller and methods described herein may be applied to autonomously controlled vehicles, remote controlled vehicles, or tele- operated vehicles.
  • the algorithms disclosed herein may also be implemented in any programming language. Use of the algorithm and methods described herein may allow a vehicle to better follow paths when some of the path segments consist of clothoids and when steering is rate-limited and may also provide more meaningful tuning parameters for the path controller by making the off-path, normal error dynamics linear, and therefore easier to understand.
  • a drivable path plan system for an autonomous vehicle may include an original path plan module that receives original path plan data including a first path element tangentially connected to a second path element at a transition connection point.
  • the drivable path plan system may also include a drivable path plan module that calculates a drivable path plan for the autonomous vehicle between the first path element and the second path element using a clothoid spline.
  • a connection point identification module may also be used to identify an initial connection point, an initial heading, and an initial curvature along the first path element, as well as a final connection point, a final heading, and a final curvature along the second path element.
  • the clothoid spline may be inserted between the initial connection point along the first path element and the final connection point along the second path element.
  • a method of calculating a drivable path plan for an autonomous vehicle may include receiving original path plan data that includes a first path element tangentially connected to a second path element at a transition connection point. The method may also include calculating a drivable path plan for the autonomous vehicle between the first path element and the second path element using a clothoid spline. The method may identify an initial connection point, an initial heading, and an initial curvature along the first path element, as well as a final connection point, a final heading, and a final curvature along the second path element. The method may also insert the calculated clothoid spline between the initial connection point along the first path element and the final connection point along the second path element.
  • a computer program product for calculating a drivable path plan for an autonomous vehicle may include a nontransitory computer readable medium and computer program code, encoded on the nontransitory computer readable medium, configured to cause at least one processor to perform the steps of: receiving original path plan data including a first path element tangentially connected to a second path element at a transition connection point, and calculating a drivable path plan for the autonomous vehicle between the first path element and the second path element using a clothoid spline by identifying an initial connection point, a n initial heading, and an initial curvature along the first path element, as well as a final connection point, a final heading, and a final curvature along the second path element.
  • the calculated clothoid spline may be inserted between the initial connection point along the first path element and the final connection point along the second path element.
  • the original path plan could be made directly with clothoid segments that connect together tangentially with matching curvatures resulting in a one step process.
  • a path controller for guiding an autonomous vehicle along a desired path may include an input module that may receive input signals such as, a normal error signal that indicates an off-path deviation of the autonomous vehicle relative to a desired path, a heading signal, and a curvature signal associated with the autonomous vehicle.
  • the path controller may also include a curvature rate module that calculates a curvature rate output signal to guide the autonomous vehicle along the desired path and a communication module that communicates the curvature rate output signal to a steering control system.
  • a method of controlling an autonomous vehicle may include receiving input signals such as, a normal error signal that indicates an off-path deviation of the autonomous vehicle relative to a desired path, a heading signal, and a curvature signal associated with the autonomous vehicle.
  • the method may also include calculating a curvature rate output signal based on the input signals that may guide the autonomous vehicle along the desired path and communicating the curvature rate output signal to a steering control system associated with the autonomous vehicle.
  • a computer program product for controlling an autonomous vehicle may include a nontransitory computer readable medium and computer program code, encoded on the nontransitory computer readable medium, configured to cause at least one processor to perform the steps of: receiving input signals such as, a normal error signal that indicates an off-path deviation of the autonomous vehicle relative to a desired path, a heading signal, and a curvature signal associated with the autonomous vehicle, as well as calculating a curvature rate output signal based on the input signals that may guide the autonomous vehicle along the desired path, and communicating the curvature rate output signal to a steering control system associated with the autonomous vehicle.
  • FIG. 1 illustrates a graphical top view of an autonomous vehicle executing a "right turn-straight line-left turn” Dubins path
  • FIG. 2 illustrates a graphical top view of an autonomous vehicle executing a "right turn-straight line-right turn" Dubins path
  • FIG. 3 illustrates a graphical top view of an autonomous vehicle executing a "left turn-straight line-left turn" Dubins path
  • FIG. 4 illustrates a block diagram of an example autonomous vehicle communication and control system of the present disclosure
  • FIG. 5 is a graphical illustration of a clothoid fu nction
  • FIG. 6 is an illustration of a reference frame with notations that define normal error path tracking for an Ackerman style vehicle
  • FIG. 7 is a graphical illustration of a rate-limited steering step response for a vehicle
  • FIG. 8 is a graphical illustration of the derivative of the rate-limited steering step response of FIG. 7;
  • FIG. 9 is an illustration of a slalom path with two tangentially connected circular arcs, that may be executed by a vehicle as a right turn-left turn Dubins style path plan;
  • FIG. 10 is a graphical illustration of the off-path error for an optimal transition from the midpoint between the two arcs in FIG. 9 to a final position along the second arc;
  • FIG. 11 is a graphical illustration of the curvature for the optimal transition from the midpoint between the two arcs in FIG. 9 to the final position along the second arc;
  • FIG. 12 is a graphical illustration of the off-path error for an optimal transition from the midpoint between the two arcs in FIG. 9 to an initial position along the first arc;
  • FIG. 13 is a graphical illustration of the curvature for the optimal transition from the midpoint between the two arcs in FIG. 9 to the initial position along the first arc;
  • FIG. 14 is a graphical illustration of an optimal transition path plan between first and second arcs;
  • FIG. 15 illustrates a block diagram of a drivable path plan system, according to one embodiment of the present disclosure
  • FIG. 16 illustrates a method of calculating a drivable path plan, according to one embodiment of the present disclosure
  • FIG. 17 illustrates a control loop flow diagram, according to one embodiment of the present disclosure
  • FIG. 18 illustrates a block diagram of a path controller, according to one embodiment of the present disclosure
  • FIG. 19A illustrates a method of controlling a vehicle with a curvature rate output signal, according to one embodiment of the present disclosure.
  • FIG. 19B illustrates a method of optimizing a path controller, according to embodiments of the present disclosure.
  • An adjustable parameter may be provided to control the tradeoff between accuracy and smoothness when making a transition between two arc segments or between an a rc and line segment.
  • a pair of tangentially connected arc segments or a tangentially connected arc segment and line segment are provided as an original or desired path plan. Between these two segments, three clothoid segments are inserted. These clothoid segments connect tangentially to the original segments and to each other. In addition, curvatures match at each point where there is a connection. Finally, in addition to connection, tangency, and curvature constraints being satisfied, the clothoids may be chosen to minimize the maximum excursion from the original path (e.g., the off-path normal error), defined by of the pair of tangentially connected arc segments or the tangentially connected arc segment and line segment.
  • the new path plan with the clothoid transitions can be driven by a vehicle with rate-limited steering, whereas the original desired path plan cannot be driven by a vehicle with rate-limited steering because the steering wheel angle cannot be instantaneously changed when transitions occur. This places a burden on the path tracking algorithm, which must do the best possible job of handling abrupt changes in path curvature.
  • the new path plan is a drivable path plan that may be as close as possible to the original path plan. In this manner, the off-path normal error may be minimized with respect to the original path, which consists of simple arcs and/or line segments.
  • the output of the controller may be a curvature rate, instead of a simple constant curvature, which may make it possible to better track paths with changing curvature values, such as clothoid segments.
  • Adjustable parameters may also be provided to control tradeoffs between accuracy and smoothness when making transitions between path segments and the controller algorithms described herein may better handle steering rate limits associated with steering actuators than prior controllers.
  • Controllers described herein may also use nonlinear feedforward to output linearize the system to produce straightforward, intuitive dynamic behavior (transients), as well as stabilize the system by applying the LQR methods to the controller.
  • a controller and/or algorithm may combine a curvature rate output, output linearization with respect to normal error, and LQR to better track arc, line, and clothoid path segments with respect to off-path, normal error.
  • FIGS. 1-3 illustrate various graphical top views of an autonomous vehicle 110, 210, 310 executing different path plans made of simple circular arcs and straight lines, known as Dubins paths.
  • FIG. 1 shows the autonomous vehicle 110 executing a "right turn- straight line-left turn” Dubins path
  • FIG. 2 shows the autonomous vehicle 210 executing a "right turn-straight line-right turn” Dubins path
  • FIG. 3 shows the autonomous vehicle 310 executing a "left turn-straight line-left turn” Dubins path.
  • a Dubins path typically refers to the shortest curve that connects two points in the two-dimensional Euclidean plane (e.g., the "x-y" plane) with a constraint on the curvature of the path and prescribed initial and terminal tangents to the path.
  • a Dubins path typically consists of maximum curvature arcs and/or straight line segments to achieve the shortest feasible path between two destinations for an autonomous vehicle.
  • the optimal path type can be described as a vehicle making a combination of 'right turns (R)', 'left turns (L)' or driving 'straight (S).'
  • an optimal Dubins path will always be at least one of the following six types: RSR, RSL, LSR, LSL, RLR, and LRL.
  • FIG. 1 illustrates an autonomous vehicle 110 executing an RSL Dubins path 100, corresponding to a first arc 140 (right turn) that lies on a first circle 120, followed by a straight line segment 160, followed by a second arc 175 (left turn) that lies on a second circle 122.
  • the autonomous vehicle 110 may start at an initial point "A" 130 along the first arc 140 with an initial heading 135.
  • the autonomous vehicle 110 may transition to the straight line segment 160.
  • the autonomous vehicle 110 may transition to the second arc 175 and follow this arc until it reaches its final destination at a final point "B" 180 along the second arc 175 where it will have a final heading 185.
  • FIG. 2 illustrates an autonomous vehicle 210 executing an RSR Dubins path 200, corresponding to a first arc 240 (right turn) that lies on a first circle 220, followed by a straight line segment 260, followed by a second arc 275 (right turn) that lies on a second circle 222.
  • the autonomous vehicle 210 may start at an initial point "A" 230 along the first arc 240 with an initial heading 235.
  • the autonomous vehicle 210 may transition to the straight line segment 260.
  • the autonomous vehicle 210 may transition to the second arc 275 and follow this arc until it reaches its final destination at a final point "B" 280 along the second arc 275 where it will have a final heading 285.
  • FIG. 3 illustrates a n autonomous vehicle 310 executing an LRL Dubins path 300, corresponding to a first arc 340 (left turn) that lies on a first circle 320, followed by a second arc 360 (right turn) that lies on a second circle 322, followed by a third arc 375 (left turn) that lies on a third circle 324.
  • the autonomous vehicle 310 may start at an initial point "A" 330 along the first arc 340 with an initial heading 335.
  • the autonomous vehicle 310 may transition to the second arc 360 and follow this arc until it reaches the second transition connection point 370.
  • the autonomous vehicle 310 may then transition to the third arc 375 and follow this arc until it reaches its final destination at a final point "B" 380 along the third arc 375 where it will have a final heading 385.
  • FIG. 4 is a schematic diagram of an example communication and control system 400 that may be utilized in conjunction with the systems and methods of the present disclosure, in at least some embodiments.
  • the communication and control system 400 may include a vehicle control system 440 which may be mounted on the autonomous vehicle 410.
  • the autonomous vehicle 410 may also include a spatial locating device 442, which may be mounted to the autonomous vehicle 410 and configured to determine a position of the autonomous vehicle 410 as well as a heading and a speed of the autonomous vehicle 410.
  • the spatial locating device 442 may include any suitable system configured to determine the position and/or other important characteristics of the autonomous vehicle 410, such as a global positioning system (GPS), a global navigation satellite system (GNSS), or the like.
  • GPS global positioning system
  • GNSS global navigation satellite system
  • the spatial locating device 442 may be configured to determine the position and/or other characteristics of the autonomous vehicle 410 relative to a fixed point within a field (e.g., via a fixed radio transceiver). Accordingly, the spatial locating device 442 may be configured to determine the position of the autonomous vehicle 410 relative to a fixed global coordinate system using GPS, GNSS, a fixed local coordinate system, or any combination thereof.
  • the autonomous vehicle 410 may include a steering control system 444 configured to control a direction of movement of the autonomous vehicle 410, and a speed control system 446 configured to control a speed of the autonomous vehicle 410.
  • the autonomous vehicle 410 may include an implement control system 448 configured to control operation of an implement towed by the autonomous vehicle 410 or integrated within the autonomous vehicle 410.
  • the control system 440 may further include a controller 450 communicatively coupled to the spatial locating device 442, the steering control system 444, the speed control system 446, and the implement control system 448.
  • the controller 450 may be configured to receive signals relative to many parameters of interest including, but not limited to: vehicle position, vehicle speed, vehicle heading, desired path location, off-path normal error, desired off-path normal error, vehicle state vector information, curvature state vector information, turning radius limits, steering angle, steering angle limits, steering rate limits, curvature, curvature rate, rate of curvature limits, roll, pitch, rotational rates, acceleration, and the like, or any combination thereof.
  • the controller 450 may be an electronic controller with electrical circuitry configured to process data from the spatial locating device 442, among other components of the autonomous vehicle 410.
  • the controller 450 may include a processor, such as the illustrated microprocessor 454, and a memory device 456.
  • the controller 450 may also include one or more storage devices and/or other suitable components (not shown).
  • the processor 454 may be used to execute software, such as software for calculating drivable path plans.
  • the processor 454 may include multiple microprocessors, one or more "general- purpose" microprocessors, one or more special-purpose microprocessors, and/or one or more application specific integrated circuits (ASICS), or any combination thereof.
  • ASICS application specific integrated circuits
  • the processor 454 may include one or more reduced instruction set (RISC) processors.
  • RISC reduced instruction set
  • the memory device 456 may include a volatile memory, such as random access memory (RAM), and/or a nonvolatile memory, such as ROM.
  • the memory device 456 may store a variety of information and may be used for various purposes.
  • the memory device 456 may store processor-executable instructions (e.g., firmware or software) for the processor 454 to execute, such as instructions for calculating drivable path plans, and/or controlling the autonomous vehicle 410.
  • the memory device 456 may include flash memory, one or more hard drives, or any other suitable optical, magnetic, or solid-state storage medium, or a combination thereof.
  • the memory device 456 may store data such as field maps, maps of desired paths, vehicle characteristics, software or firmware instructions, and/or any other suitable data.
  • the steering control system 444 may include a curvature rate control system 460, a differential braking system 462, and a torque vectoring system 464 that may be used to steer the autonomous vehicle 410.
  • the curvature rate control system 460 may control a direction of an autonomous vehicle 410 by actuating a servo controlled steering system associated with the autonomous vehicle 410 with a curvature rate signal, such as an Ackerman style autonomous vehicle 410.
  • the curvature rate control system 460 may automatically rotate one or more wheels or tracks of the autonomous vehicle 410 via hydraulic actuators to steer the autonomous vehicle 410.
  • the curvature rate control system 460 may rotate front wheels/tracks, rear wheels/tracks, and/or intermediate wheels/tracks of the autonomous vehicle 410, either individually or in groups.
  • the differential braking system 462 may independently vary the braking force on each lateral side of the autonomous vehicle 410 to direct the autonomous vehicle 410.
  • the torque vectoring system 464 may differentially apply torque from the engine to the wheels and/or tracks on each lateral side of the autonomous vehicle 410.
  • the illustrated steering control system 444 includes the curvature rate control system 460, the differential braking system 462, and the torque vectoring system 464, it should be appreciated that alternative embodiments may include one or more of these systems, in any suitable combination. Further embodiments may include a steering control system 444 having other and/or additional systems to facilitate turning the autonomous vehicle 410 such as an articulated steering system, a differential drive system, and the like.
  • the speed control system 446 may include an engine output control system 466, a transmission control system 468, and a braking control system 470.
  • the engine output control system 466 may be configured to vary the output of the engine to control the speed of the autonomous vehicle 410.
  • the engine output control system 466 may vary a throttle setting of the engine, a fuel/air mixture of the engine, a timing of the engine, and/or other suitable engine parameters to control engine output.
  • the transmission control system 468 may adjust gear selection within a transmission to control the speed of the autonomous vehicle 410.
  • the braking control system 470 may adjust braking force to control the speed of the autonomous vehicle 410.
  • speed control system 446 includes the engine output control system 466, the transmission control system 468, and the braking control system 470, it should be appreciated that alternative embodiments may include one or more of these systems, in any suitable combination. Further embodiments may include a speed control system 446 having other and/or additional systems to facilitate adjusting the speed of the autonomous vehicle 410.
  • the implement control system 448 may be configured to control various parameters of the implement towed by and/or integrated within the autonomous vehicle 410.
  • the implement control system 448 may be configured to instruct an implement controller via a communication link, such as a CAN bus or ISOBUS, to adjust a penetration depth of at least one ground engaging tool of an agricultural implement, which may reduce the draft load on the autonomous vehicle 410.
  • the implement control system 448 may instruct the implement controller to transition the agricultural implement between a working position and a transport position, to adjust a flow rate of product from the agricultural implement, to adjust a position of a header of the agricultural implement (e.g., a harvester, etc.), among other operations.
  • the operator interface 452 may be communicatively coupled to the controller 450 and configured to present data from the autonomous vehicle 410 via a display 472.
  • Display data may include: data associated with operation of the autonomous vehicle 410, data associated with operation of an implement, a position of the autonomous vehicle 410, a speed of the autonomous vehicle 410, a desired path, a drivable path plan, a target position, a current position, etc.
  • the operator interface 452 may be configured to enable an operator to control certain functions of the autonomous vehicle 410 such as starting and stopping the autonomous vehicle 410, inputting a desired path, etc. I n some embodiments, the operator interface 452 may enable the operator to input parameters that cause the controller 450 to adjust the drivable path plan.
  • the operator may provide an input requesting that the desired path be acquired as quickly as possible, that an off-path normal error be minimized, that a speed of the autonomous vehicle 410 remain within certain limits, that a lateral acceleration experienced by the autonomous vehicle 410 remain within certain limits, etc.
  • the operator interface 452 may be configured to alert an operator if the desired path cannot be achieved (e.g., via the display 472, or via an audio system (not shown), etc., for example).
  • the control system 440 may include a base station 474 having a base station controller 476 located remotely from the autonomous vehicle 410.
  • control functions of the control system 440 may be distributed between the controller 450 of the autonomous vehicle control system 440 and the base station controller 476.
  • the base station controller 476 may perform a substantial portion of the control functions of the control system 440.
  • a first transceiver 478 positioned on the autonomous vehicle 410 may output signals indicative of vehicle characteristics (e.g., position, speed, heading, curvature rate, curvature rate limits, maximum turning rate, minimum turning radius, steering angle, roll, pitch, rotational rates, acceleration, etc.) to a second transceiver 480 at the base station 474.
  • the base station controller 476 may calculate drivable path plans and/or output control signals to control the curvature rate control system 444, the speed control system 446, and/or the implement control system 448 to direct the autonomous vehicle 410 toward the desired path, for example.
  • the base station controller 476 may include a processor 482 and memory device 484 having similar features and/or capabilities as the processor 454 and the memory device 456 discussed previously.
  • the base station 474 may include an operator interface 486 having a display 488, which may have similar features and/or capabilities as the operator interface 452 and the display 472 discussed previously.
  • a planar parametric curve may be given by:
  • B is the magnitude of the clothoid.
  • the magnitude of the clothoid spline B may correspond to a speed and a constant maximum curvature rate of a given autonomous vehicle.
  • FIG. 6 illustrates a vehicle model and reference frame 600 with notations that generally define normal error path tracking for an Ackerman style vehicle 605 having a rear axle
  • the vehicle model shown in FIG. 6 may be simplified as a kinematic bicycle model, which gives:
  • ⁇ is the heading of the vehicle
  • ⁇ is the speed of the rear axle
  • L is the wheelbase
  • ⁇ is the angle of the steered front wheels (not to be confused with the used later for slalom
  • the above kinematic model of the vehicle includes a curvature state and the input to the model is the curvature rate.
  • the following constraints imposed may be imposed: and the states may be expressed as .
  • the curvature rate input may be defined as
  • the curvature rate may be closely related to the steering rate of the vehicle.
  • a slightly nonlinear relationship may exist between the curvature rate and the steering rate of a given vehicle, due to the steering linkage geometry associated with the vehicle.
  • the steering linkage on a typical Ackerman vehicle may introduce a nonlinear relationship between the curvature rate and the steering rate of the vehicle. Since the turning radius of the vehicle is:
  • vehicle is parallel to the circular path and is pointed in the counter-clockwise direction. Similarly means the vehicle is parallel to the path and is pointed in the clockwise direction.
  • FIG. 7 is a graphical illustration of a rate-limited steering step response for an example Ackerman style vehicle (not shown).
  • the steering rate limit for the example Ackerman style vehicle may not have a perfectly uniform slope, or rate of curvature, as the step response moves between -100% to 100% steering change.
  • This nonlinearity may become more evident when viewing a derivative of the steering step response, as can be seen in FIG. 8, which shows the change in slope resulting in a somewhat "bowl shape" toward the extreme curvature edges. This may be due to nonlinearity in the steering linkage of the vehicle that may produce more curvature change at the extreme curvature ranges.
  • the slope, or rate of curvature may be sufficiently constant close to the center of the steering angle travel, such that it may be approximated as having a relatively constant value.
  • n is a positive integer.
  • FIG. 9 is an illustration of a slalom path with two tangentially connected circular arcs that may be executed by a vehicle as a right turn- left turn Dubins style path plan.
  • the initial conditions at the origin are given by:
  • FIGS.10 and 11 illustrate the solution graphically, with FIG.10 showing the off-path error for an optimal transition from the midpoint between the two arcs in FIG. 9 to a final position along the second arc and FIG.11 showing the curvature for the optimal transition from the midpoint between the two arcs in FIG. 9 to the final position along the second arc.
  • the maximum error shown if FIG.10 is about 0.028 m and the switch from a positive curvature rate to a negative curvature rate takes place at about 1.23 seconds, as can be seen in FIG.11.
  • a second method may be to start on the first arc and end at the origin.
  • the initial conditions in this case would be:
  • FIGS.12 and 13 illustrate the solution graphically, with FIG.12 showing the off-path error for an optimal transition from the midpoint between the two arcs in FIG. 9 to an initial position along the first arc, and FIG. 13 showing the curvature for the optimal transition from the midpoint between the two arcs in FIG. 9 to the initial position along the first arc.
  • the maximum error shown if FIG. 12 is about 0.028 m and the switch from a negative curvature rate to a positive curvature rate takes place at about 0.32 seconds, as can be seen in FIG. 13.
  • a third method of solving this problem is fundamentally different than the prior two methods. Rather than using optimal control theory, an approach based on parametric curves, nonlinear equation solving, and minimization may be used in this method. Given that an optimal transition involves changing the curvature at a constant maximum rate, the segments are clothoids with a magnitude given by the vehicle speed and maximum curvature rate. A clothoid can be rotated and translated. This involves three parameters. Further, the starting and ending parameter can be chosen, and this adds a n additional two parameters. Thus, a clothoid segment is associated with five parameters.
  • Constraining a position gives two parameters, and constraining the tangent direction and the curvature give two more constraint parameters.
  • an additional five parameters of freedom are added but four constraint equations are also added.
  • the optimal control approach tells us that there should be three clothoid segments inserted between two arcs. This leads to sixteen equations and seventeen unknowns. The extra degree of freedom can be adjusted to optimize the solution.
  • FIG. 14 illustrates another example of an optimal transition path plan solution 1400 between a tangentially connected first arc 1410 and second arc 1420, where B— 5.5, the first arc 1410 is centered at the origin, and the second arc 1420 is centered at (20, 0).
  • FIG. 14 shows the solution generated by the third method with a first portion of a drivable path plan solution 1430 shown diverging away from the first arc 1410 from some initial point and dipping below the first arc 1410. The drivable path plan then passes through the transition connection point where the first and second arcs 1410, 1420 tangentially connect to each other. A second portion of the drivable path plan solution 1440 can then be seen rising above the second arc 1420, and reconnecting with the second arc 1420 at some final point along the second arc 1420.
  • the third method includes calculating a drivable path plan for an original path plan that includes a first path element tangentially connected to a second path element at some transition connection point.
  • a drivable path plan may be calculated to find an optimal clothoid spline between the first path element and the second path element using the third method described above.
  • the third method identifies an initial connection point, an initial heading, and an initial curvature along the first path element, as well as a final connection point, a final heading, and a final curvature along the second path element.
  • the clothoid spline Once the clothoid spline is calculated, it may be inserted between the initial connection point along the first path element and the final connection point along the second path element to create a drivable path plan for an autonomous vehicle to follow.
  • FIG. 15 illustrates a block diagram of a drivable path plan system 1500, according to one embodiment of the present disclosure.
  • the drivable path plan system 1500 may generally include an original path plan module 1510, off-path error data 1550, vehicle data 1560, original path plan data 1530, a drivable path plan module 1520, and clothoid spline data 1540.
  • the off-path error data 1550 may include data that indicates an off-path deviation of the autonomous vehicle relative to a desired original path plan.
  • the vehicle data 1560 may include data about the autonomous vehicle, including but not limited to: vehicle position, vehicle speed, vehicle heading, desired path location, off-path normal error, desired off-path normal error, vehicle state vector information, curvature state vector information, turning radius limits, steering angle, steering angle limits, steering rate limits, curvature, curvature rate, rate of curvature limits, roll, pitch, rotational rates, acceleration, and the like.
  • the original path plan module 1510 may be configured to receive original path plan data 1530 indicative of an original path plan for an autonomous vehicle.
  • the original path plan may include data relating to a first path element, such as a first circular arc having a first circular arc curvature, as well as a second path element that is tangentially connected to the first path element at a transition connection point.
  • the original path plan data may include first path element data 1532, second path element data 1534, and transition connection point data 1536.
  • the drivable path plan module 1520 may be configured to calculate a drivable path plan for the autonomous vehicle between the first path element a nd the second path element which may include a clothoid spline formed of three clothoid segments, as previously discussed.
  • the drivable path plan module 1520 may also include a connection point identification module 1522 and a clothoid spline calculation module 1524.
  • connection point identification module 1522 may be configured to identify a suitable initial connection point, an initial heading, and an initial curvature along the first path element, as well as a suitable final connection point, a final heading, and a final curvature along the second path element. These connection points, headings, and curvatures help define a set of suitable clothoid spline solutions between the selected initial connection point and final connection point, as previously discussed. Accordingly, the clothoid spline data 1540 may include initial connection point data 1542, final connection point data 1544, and optimal transition data 1545, which may comprise other data such as initial heading data, initial curvature data, final heading data, final curvature data, and the like.
  • the clothoid spline calculation module 1524 may be configured to calculate a suitable clothoid spline between the initial connection point on the first path element and the final connection point on the second path element.
  • the clothoid spline may include a first clothoid segment, a second clothoid segment, and a third clothoid segment connected together to form the clothoid spline.
  • Corresponding first clothoid segment data 1546, second clothoid segment data 1548, and third clothoid segment data 1549 may be stored as clothoid spline data 1540 and may define one or more suitable clothoid spline solutions.
  • the first clothoid segment may be selected to be tangentially connected to the first path element at the initial connection point.
  • the first clothoid segment may have a first clothoid segment initial curvature and a first clothoid segment final curvature.
  • the first clothoid segment initial curvature may be equal to the first circular arc curvature of the first path element at the initial connection point.
  • the second clothoid segment may be tangentially connected to the first clothoid segment at a second connection point.
  • the second clothoid segment may have a second clothoid segment initial curvature and a second clothoid segment final curvature.
  • the second clothoid segment initial curvature may be equal to the first clothoid segment final curvature at the second connection point.
  • the third clothoid segment may be tangentially connected to the second clothoid segment at a third connection point.
  • the third clothoid segment may have a third clothoid segment initial curvature and a third clothoid segment final curvature.
  • the third clothoid segment initial curvature may be equal to the second clothoid segment final curvature at the third connection point.
  • the third clothoid segment may also be tangentially connected to the second path element at the final connection point and the third clothoid segment final curvature may be equal to a second path element curvature at the final connection point.
  • the second path element may include a second circular arc having a second circular arc curvature. In other embodiments, the second path element may include a straight line.
  • the clothoid spline is selected to minimize a lateral acceleration experienced by the autonomous vehicle as the autonomous vehicle executes a drivable path plan between the first path element and the second path element.
  • the clothoid spline is selected to minimize an off-path error of the drivable path plan with respect to the original path plan.
  • FIG. 16 illustrates a method 1600 of calculating a drivable path plan for an autonomous vehicle, according to one embodiment of the present disclosure.
  • the method 1600 may begin with a step 1610 in which original path plan data is received.
  • the original path plan data may indicate an original path plan for an autonomous vehicle including a first path element including a first circular arc having a first circular arc curvature and a second path element tangentially connected to the first path element at a transition connection point.
  • the method 1600 may proceed to a step 1620 in which a suitable initial connection point, initial heading, and initial curvature along the first path element may be identified.
  • a suitable initial connection point may be chosen based on various characteristics of the vehicle including, but not limited to: vehicle position, vehicle speed, vehicle heading, desired path location, off-path normal error, desired off-path normal error, vehicle state vector information, curvature state vector information, turning radius limits, steering angle, steering angle limits, steering rate limits, curvature, curvature rate, rate of curvature limits, roll, pitch, rotational rates, acceleration, and the like.
  • a suitable final connection point may be chosen based on various characteristics of the vehicle including, but not limited to: vehicle position, vehicle speed, vehicle heading, desired path location, off-path normal error, desired off-path normal error, vehicle state vector information, curvature state vector information, turning radius limits, steering angle, steering angle limits, steering rate limits, curvature, curvature rate, rate of curvature limits, roll, pitch, rotational rates, acceleration, and the like.
  • the method 1600 may proceed to a step 1640 in which one or more drivable path plans may be calculated between the initial connection point along the first path element and the final connection point along the second path element, which may take the form of a clothoid spline made of three clothoid segments, as previously discussed.
  • the method 1600 may proceed to a step 1650 in which a suitable clothoid spline may be selected to minimize a lateral acceleration experienced by the autonomous vehicle as the autonomous vehicle executes the drivable path plan between the first path element and the second path element.
  • the method 1600 may proceed to a step 1660 in which in which a suitable clothoid spline may be selected to minimize an off-path error of the drivable path plan with respect to the original path plan. It will be noted that the selection of clothoid splines, as shown in blocks 1650, 1660, may also be accomplished prior to step 1620 in method 1600.
  • Any methods disclosed herein comprise one or more steps or actions for performing the described method.
  • the method steps and/or actions may be interchanged with one another.
  • the order and/or use of specific steps and/or actions may be modified.
  • the normal error e and its first three derivatives can be derived as:
  • the first step in calculating the normal error to the clothoid is to determine the parameter ⁇ where a line from the control point (x, y) to the clothoid is normal. This requires solving a single nonlinear equation in one unknown:
  • the first term is the distance from the control point to the clothoid center of curvature.
  • the second term is the distance from the clothoid point (of normalcy) to the clothoid center of curvature. This formula is analogous to the formula for the normal error for an arc segment in equation (2).
  • FIG. 17 illustrates a control loop flow diagram 1700 that may be used with embodiments of the present disclosure described above with respect to line segments, arc segments, and clothoid segments.
  • the control loop flow diagram 1700 shows how nonlinear feedforward may be used to output linearize the path controller to produce straightforward, intuitive dynamic behavior (transients), as well as stabilize the system by applying LQR methods. This may result in a path controller that is easier to tune, due to the use of the LQR method with its use of weights on errors and reduced possibility for instability.
  • the path controller may combine curvature rate output, output linearization with respect to normal error, and LQR to better track arc, line, and clothoid path segments with respect to off- path, normal error.
  • the control loop flow diagram 1700 may include a path controller 1710 (shown in dashed lines) communicatively coupled to a vehicle model block 1720.
  • the path controller 710 may include an integrator 1780, a linearization block 1790, and gain blocks 1771, 1772, 1773 corresponding to gain values
  • the path control ler 1710 may also include inputs 1730,
  • the path controller 1710 may also include one or more additional input signals, generally referred to as "S" 1760, from other parts of the system, such as the vehicle model block 1720.
  • the input signals "S" 1760 may include, but are not limited to: a heading signal, a curvature signal, a vehicle position signal, a vehicle speed signal, a curvature state signal, a vehicle state vector signal, a desired error signal (where the desired error signal indicates a desired off-path deviation of the autonomous vehicle relative to the desired path, where the desired path includes at least one of a straight line, an arc, and a clothoid segment), and the like.
  • the path controller 1710 may produce a curvature rate output signal " u " 1792 which may be used by a steering control system to control a curvature rate associated with a steering system of an autonomous vehicle.
  • the curvature rate output signal 1792 may be calculated based on the input signals received at the path controller 1710 to linearize normal error dynamics in relation to the desired path by integrating the normal error signal received at the path controller 1710 with one or more derivatives of the normal error signal.
  • the normal error signal 1793 may be integrated together with its first and second derivatives by the integrator 1780 to produce an output " q " 1791 that may then be fed into the linearization block 1790 to linearize the curvature rate output signal u 1792.
  • This linearization technique is similar for each path segment type (e.g., line, arc, or clothoid segment), but with different equations substituted for each path segment type in order to output linearize the curvature rate output signal u according to each path segment type.
  • the gain blocks 1771, 1772, 1773 with corresponding gain values may be ascertained via an LQR process.
  • the curvature rate output signal u 1792 may be calculated based on the input signals received at the path controller 1710 and at least one gain value nnay be applied to feedback relating to the normal error signal 1793 to stabilize the curvature rate output signal 1792, where the at least one gain value is selected using a linear quadratic regulator process.
  • FIG. 18 illustrates a block diagram of a path controller 1800 configured to guide an autonomous vehicle along a desired path, according to one embodiment of the present disclosure.
  • the path controller 1800 may include an input module 1810, a curvature rate module 1830, a communication module 1820, and a data module 1840.
  • the input module 1810 may be configured to receive input signals at the path controller 1800 such as: a normal error signal associated with the autonomous vehicle that indicates an off-path deviation of the autonomous vehicle relative to a desired path; a heading signal associated with the autonomous vehicle that indicates a current heading angle of the autonomous vehicle; and a curvature signal associated with the autonomous vehicle that indicates a curvature associated with a current turn radius of the autonomous vehicle.
  • the input module 1810 may also be configured to receive additional input signals, including, but not limited to: a heading signal, a vehicle position signal, a vehicle speed signal, a curvature state signal, a vehicle state vector signal, a desired error signal (where the desired error signal indicates a desired off-path deviation of the autonomous vehicle relative to the desired path), as well as other input signals.
  • additional input signals including, but not limited to: a heading signal, a vehicle position signal, a vehicle speed signal, a curvature state signal, a vehicle state vector signal, a desired error signal (where the desired error signal indicates a desired off-path deviation of the autonomous vehicle relative to the desired path), as well as other input signals.
  • input signals may be stored in the data module 1840 as input data 1850 including, but not limited to: normal error data 1851, heading data 1852, curvature data 1853, vehicle position data 1854, vehicle speed data 1855, vehicle state data 1856, curvature state data 1857, desired error data 1858, along with other data such as, vehicle data 1880, steering limit data 1882, steering rate limit data 1884, path type data 1890, line data 1892, arc data, 1894, clothoid segment data 1896, gain data 1870, gain value(s) 1872, curvature rate output data 1860, as well as other any other relevant data.
  • the curvature rate module 1830 may be configured to calculate a curvature rate output signal 1822 based on the input signals received at the path controller 1800, and the curvature rate output signal 1822 may be configured to guide the autonomous vehicle along a desired path.
  • the curvature rate module 1830 may include a linearization module 1832 and a gain module 1834.
  • the linearization module 1832 may be configured to linearize normal error dynamics in relation to the desired path by integrating one or more derivatives of the normal error signal received at the path controller, as previously discussed.
  • the gain module 1834 may also be configured to apply at least one gain value 1872 to feedback relating to the normal error signal to stabilize the curvature rate output signal 1822, where the at least one gain value 1872 is selected using a linear quadratic regulator process, as previously discussed.
  • the communication module 1820 may be configured to communicate the curvature rate output signal 1822 to a steering control system (not shown) that may be configured to receive the curvature rate output signal 1822 and actuate a steering system (not shown) associated with the autonomous vehicle based on the curvature rate output signal 1822 received from the path controller 1800.
  • FIG. 19A illustrates a flowchart of a method 1900 by which a curvature rate output signal may be used to control an autonomous vehicle along a desired path, according to one embodiment of the present disclosure.
  • the method 1900 may begin with a step 1910 in which input signals may be received at the path controller such as, a normal error signal, a heading signal, and a curvature signal, as well as other input signals as previously discussed.
  • the method 1900 may proceed to a step 1920 in which a curvature rate output signal may be calculated based on the input signals received at the path controller.
  • the curvature rate output signal may be configured to guide the autonomous vehicle along the desired path.
  • the method 900 may proceed to a step 1930 in which the curvature rate output signal may be communicated to a steering control system that may be configured to receive the curvature rate output signal and actuate a steering system associated with the autonomous vehicle based on the curvature rate output signal received from the path controller, and the method 1900 may end.
  • FIG. 19B illustrates a flowchart of a method 1950 by which a path controller may be linearized and optimized, according to embodiments of the present disclosure.
  • the method 1950 may begin with a step 1960 in which the normal error dynamics may be linearized in relation to the desired path by integrating one or more derivatives of the normal error signal received at the path controller, as previously discussed.
  • the method 1950 may proceed to a step 1970 in which a linear quadratic regulator process may be utilized to select at least one gain value to stabilize the curvature rate output signal.
  • the method 1950 may proceed to a step 1980 in which the selected at least one gain value may be applied to feedback relating to the normal error signal to stabilize the curvature rate output signal, and the method 1950 may end.
  • Any methods disclosed herein comprise one or more steps or actions for performing the described method.
  • the method steps and/or actions may be interchanged with one another.
  • the order and/or use of specific steps and/or actions may be modified.
  • the phrases “connected to,” “coupled to,” and “in communication with” refer to any form of interaction between two or more entities, including mechanical, electrical, magnetic, electromagnetic, fluid, and thermal interaction. Two components may be functionally coupled to each other even though they are not in direct contact with each other.
  • the term “abutting” refers to items that are in direct physical contact with each other, although the items may not necessarily be attached together.
  • the phrase “fluid communication” refers to two features that are connected such that a fluid within one feature is able to pass into the other feature.

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  • Automation & Control Theory (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)
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Abstract

L'invention concerne des systèmes, des appareils et des procédés de plans de trajet pouvant être parcourus pour des véhicules autonomes, qui peuvent recevoir des données de plan de trajet d'origine et créer un plan de trajet pouvant être parcouru pour le véhicule autonome entre un premier élément de trajet et un second élément de trajet à l'aide d'une spline de clothoïde. Un point de connexion initial peut être identifié, ainsi qu'un cap initial et une courbure initiale le long du premier élément de trajet, et un point de connexion final, un cap final, et une courbure finale le long du second élément de trajet. La spline de clothoïde peut être insérée entre le point de connexion initial le long du premier élément de trajet et le point de connexion final le long du second élément de trajet. Un contrôleur de trajet peut guider le véhicule le long du trajet de spline de clothoïde pouvant être parcouru à l'aide d'un signal de sortie de taux de courbure pour commander un système de direction du véhicule autonome.
PCT/US2016/029256 2015-04-24 2016-04-25 Système, appareil et procédé permettant de commander un véhicule WO2016172729A1 (fr)

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CN110031878A (zh) * 2017-11-30 2019-07-19 小松美国公司 车辆导引显示器和路径导航方法
CN110348115A (zh) * 2019-07-09 2019-10-18 顺丰科技有限公司 一种叉车的lqr控制方法、装置、存储介质和控制器
CN110462542A (zh) * 2017-03-29 2019-11-15 三菱电机株式会社 控制交通工具的运动的系统和方法
CN110968091A (zh) * 2018-09-28 2020-04-07 通用汽车环球科技运作有限责任公司 用于自动控制车辆的系统和方法
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CN113276833A (zh) * 2021-05-11 2021-08-20 上汽通用五菱汽车股份有限公司 车辆的横向运动控制方法、控制终端及存储介质
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CN110462542A (zh) * 2017-03-29 2019-11-15 三菱电机株式会社 控制交通工具的运动的系统和方法
CN109407658A (zh) * 2017-08-18 2019-03-01 厦门雅迅网络股份有限公司 无人车的行车轨迹规划方法及计算机可读存储介质
CN110031878A (zh) * 2017-11-30 2019-07-19 小松美国公司 车辆导引显示器和路径导航方法
CN109959383A (zh) * 2017-12-25 2019-07-02 大连楼兰科技股份有限公司 一种自动泊车路径规划方法
GB2584587A (en) * 2018-09-20 2020-12-16 Jaguar Land Rover Ltd Control system for a vehicle
GB2584587B (en) * 2018-09-20 2023-01-04 Jaguar Land Rover Ltd Systems for Control of Understeer in an Autonomous Vehicle
CN110968091A (zh) * 2018-09-28 2020-04-07 通用汽车环球科技运作有限责任公司 用于自动控制车辆的系统和方法
CN110968082A (zh) * 2018-09-28 2020-04-07 广州汽车集团股份有限公司 一种自动驾驶车辆路径追踪方法及装置
CN110968082B (zh) * 2018-09-28 2023-08-08 广州汽车集团股份有限公司 一种自动驾驶车辆路径追踪方法及装置
CN110348115B (zh) * 2019-07-09 2023-11-28 顺丰科技有限公司 一种叉车的lqr控制方法、装置、存储介质和控制器
CN110348115A (zh) * 2019-07-09 2019-10-18 顺丰科技有限公司 一种叉车的lqr控制方法、装置、存储介质和控制器
CN111882281A (zh) * 2020-08-03 2020-11-03 国网江苏省电力有限公司苏州供电分公司 基于雷电预测的主动式危险品运输雷电防护系统和方法
US20220192092A1 (en) * 2020-12-18 2022-06-23 Deere & Company Position detectors for steering systems of agricultural header transport systems
US11980125B2 (en) * 2020-12-18 2024-05-14 Deere & Company Position detectors for steering systems of agricultural header transport systems
CN112947555B (zh) * 2021-02-04 2022-06-14 中国人民解放军国防科技大学 多机协同打击的快速航迹规划方法、装置及计算机设备
CN112947555A (zh) * 2021-02-04 2021-06-11 中国人民解放军国防科技大学 多机协同打击的快速航迹规划方法、装置及计算机设备
CN113276833B (zh) * 2021-05-11 2022-07-29 上汽通用五菱汽车股份有限公司 车辆的横向运动控制方法、控制终端及存储介质
CN113276833A (zh) * 2021-05-11 2021-08-20 上汽通用五菱汽车股份有限公司 车辆的横向运动控制方法、控制终端及存储介质
CN114001739A (zh) * 2021-10-11 2022-02-01 广州小鹏自动驾驶科技有限公司 一种路径规划方法、装置、车辆及存储介质
CN114001739B (zh) * 2021-10-11 2024-04-16 广州小鹏自动驾驶科技有限公司 一种路径规划方法、装置、车辆及存储介质

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