WO2022198994A1 - Procédé et appareil de planification de mouvement de bras robotisé, ainsi que support de stockage lisible et bras robotisé - Google Patents

Procédé et appareil de planification de mouvement de bras robotisé, ainsi que support de stockage lisible et bras robotisé Download PDF

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Publication number
WO2022198994A1
WO2022198994A1 PCT/CN2021/124620 CN2021124620W WO2022198994A1 WO 2022198994 A1 WO2022198994 A1 WO 2022198994A1 CN 2021124620 W CN2021124620 W CN 2021124620W WO 2022198994 A1 WO2022198994 A1 WO 2022198994A1
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Prior art keywords
pose
random
motion planning
robotic arm
random pose
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PCT/CN2021/124620
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English (en)
Chinese (zh)
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陈金亮
刘益彰
熊友军
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深圳市优必选科技股份有限公司
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B25J9/1666—Avoiding collision or forbidden zones
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J18/00—Arms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed

Definitions

  • the present application belongs to the field of robotics technology, and in particular relates to a method, device, computer-readable storage medium and a robotic arm for motion planning of a robotic arm.
  • RRT Rapidly-exploring Random Trees
  • BiRRT Bi-directional Rapidly Expanding Random Trees
  • the embodiments of the present application provide a robotic arm motion planning method, device, computer-readable storage medium, and robotic arm to solve the problems of lack of guidance and low efficiency in the existing robotic arm motion planning methods.
  • a first aspect of the embodiments of the present application provides a method for planning a motion of a robotic arm, which may include:
  • the second random pose is used as a random sampling result in a preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.
  • the iterative optimization of the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose may include:
  • the first random pose is iteratively optimized according to the gravitational field function to obtain an optimized second random pose.
  • the iterative optimization of the first random pose according to the gravitational field function to obtain an optimized second random pose may include:
  • the current random pose is updated and calculated according to the gradient of the current random pose to obtain an updated random pose
  • the updated random pose is taken as the new current random pose, and the execution of the calculation of the current random pose according to the gravitational field function is returned to.
  • the updated random pose is determined as the optimized second random pose.
  • the establishing the gravitational field function corresponding to the virtual optimal pose may include:
  • q goal is the virtual optimal pose
  • q is the current random pose
  • d(q,q goal ) is the distance between q and q goal
  • preset distance threshold is the preset distance threshold
  • U att (q) is the gravitational field function
  • calculating the gradient of the current random pose according to the gravitational field function may include:
  • the gradient of the current random pose is calculated according to the following formula:
  • the update calculation of the current random pose according to the gradient of the current random pose to obtain an updated random pose may include:
  • the current random pose is updated and calculated according to the following formula:
  • q is the current random pose
  • ⁇ is the preset more
  • the new step size, q' is the updated random pose.
  • determining the virtual optimal motion planning path of the robotic arm moving from the preset initial pose to the preset target pose may include:
  • a straight path connecting the initial pose and the target pose is determined as the virtual optimal motion planning path.
  • a second aspect of the embodiments of the present application provides a robotic arm motion planning device, which may include:
  • the virtual path determination module is used to determine the virtual optimal motion planning path of the robot arm moving from the preset initial pose to the preset target pose;
  • a random sampling module for performing random sampling in the free space of the robotic arm to obtain the first random pose of the robotic arm
  • an iterative optimization module configured to iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose
  • a motion planning module configured to use the second random pose as a random sampling result in a preset motion planning algorithm of the manipulator to perform motion planning for the manipulator.
  • the iterative optimization module may include:
  • a pose mapping unit configured to map the first random pose onto the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose
  • a gravitational field function establishment unit configured to establish a gravitational field function corresponding to the virtual optimal pose
  • An iterative optimization unit configured to iteratively optimize the first random pose according to the gravitational field function to obtain an optimized second random pose.
  • the iterative optimization unit may include:
  • the current random pose determination subunit is used to use the first random pose as the current random pose
  • a gradient calculation subunit configured to calculate the gradient of the current random pose according to the gravitational field function
  • an update calculation subunit configured to update and calculate the current random pose according to the gradient of the current random pose to obtain an updated random pose
  • the current random pose update subunit is configured to use the updated random pose as a new current random pose if the gradient of the current random pose does not meet the preset iteration termination condition;
  • the second random pose determination subunit is configured to determine the updated random pose as the optimized second random pose if the gradient of the current random pose satisfies the iteration termination condition.
  • the gravitational field function establishment unit is specifically configured to establish the gravitational field function according to the following formula:
  • q goal is the virtual optimal pose
  • q is the current random pose
  • d(q,q goal ) is the distance between q and q goal
  • preset distance threshold is the preset distance threshold
  • U att (q) is the gravitational field function
  • the gradient calculation subunit is specifically configured to calculate the gradient of the current random pose according to the following formula:
  • update calculation subunit is specifically configured to update and calculate the current random pose according to the following formula:
  • q is the current random pose
  • ⁇ is the preset update step size
  • q′ is the updated random pose
  • the virtual path determination module is specifically configured to determine a straight line path connecting the initial pose and the target pose as the virtual optimal motion planning path.
  • a third aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any one of the above-mentioned robotic arm motion planning methods is implemented. step.
  • a fourth aspect of the embodiments of the present application provides a robotic arm, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program The steps of implementing any one of the above-mentioned robotic arm motion planning methods.
  • a fifth aspect of the embodiments of the present application provides a computer program product, which, when the computer program product runs on a robotic arm, causes the robotic arm to execute the steps of any of the above-mentioned methods for motion planning of a robotic arm.
  • the embodiments of the present application determine a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose; Perform random sampling in the free space of the arm to obtain the first random pose of the robotic arm; iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose pose; use the second random pose as a random sampling result in a preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.
  • the virtual optimal motion planning path in the ideal state is used as the guiding basis for random sampling, and the original random pose (ie the first random pose) obtained by random sampling is iteratively optimized, so that it is transferred to the virtual Traction in the direction of the optimal motion planning path to obtain the optimized random pose (ie, the second random pose), and the random sampling result obtained in this way has obvious orientation.
  • the motion planning of the robotic arm is carried out. , which can reduce a large number of invalid searches and greatly improve the planning efficiency.
  • FIG. 1 is a flowchart of an embodiment of a method for planning a motion of a robotic arm in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of iterative optimization of a first random pose according to a virtual optimal motion planning path
  • FIG. 3 is a schematic flowchart of iterative optimization of the first random pose according to the gravitational field function
  • FIG. 4 is a structural diagram of an embodiment of a robotic arm motion planning device in an embodiment of the application.
  • FIG. 5 is a schematic block diagram of a robotic arm in an embodiment of the present application.
  • the term “if” may be contextually interpreted as “when” or “once” or “in response to determining” or “in response to detecting” .
  • the phrases “if it is determined” or “if the [described condition or event] is detected” may be interpreted, depending on the context, to mean “once it is determined” or “in response to the determination” or “once the [described condition or event] is detected. ]” or “in response to detection of the [described condition or event]”.
  • an embodiment of a robotic arm motion planning method in the embodiment of the present application may include:
  • Step S101 determining a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose.
  • the set composed of the poses of all joints of the manipulator can be described by the configuration space, that is, the C space, and when the manipulator and obstacles are considered, the C space is divided into two spaces , namely the obstacle space C Obs and the free space C free .
  • C Obs can be described by the following formula:
  • C Obs is a set of poses that satisfy the following conditions: the pose (ie q) belongs to the C space and the robot arm (ie Robot(q)) under this pose and the obstacle (ie Obs) do not intersect empty, the two collide.
  • C free can be described by the following formula:
  • C free is the complement of C Obs .
  • C free is an optional area in which the robotic arm can move and find the optimal trajectory and the shortest time.
  • the basic idea of the manipulator motion planning algorithm is to establish a path diagram for solving the manipulator motion planning problem based on C free .
  • the specific process can include:
  • the collision-free path ⁇ of the manipulator is obtained by sampling according to the motion planning algorithm of the manipulator in the free space C free , and the path must satisfy ⁇ [0,1] ⁇ C free , where 0 represents the starting time point of the motion planning , 1 indicates the termination time point of the motion planning, the robot arm pose at any planning time point between the start time point and the termination time point of the path is within the range of the free space C free , and the path
  • a straight path connecting the initial pose and the target pose may be determined as the virtual optimal motion planning path.
  • this virtual optimal motion planning path is only an optimal path in an ideal state, because this path may pass through the obstacle space, so it is often not feasible in practice.
  • the path can be used as the guiding basis for the actual motion planning, reducing the random search without direction in the process of motion planning.
  • step S102 random sampling is performed in the free space of the manipulator to obtain a first random pose of the manipulator.
  • random sampling in free space may be performed in any manner in the prior art.
  • the result obtained by random sampling using the prior art is recorded as the first random pose.
  • Step S103 iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose.
  • step S103 may specifically include the following process:
  • Step S1031 mapping the first random pose to the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose.
  • the projection of the first random pose on the virtual optimal motion planning path may be used as the virtual optimal pose.
  • Step S1032 establishing a gravitational field function corresponding to the virtual optimal pose.
  • the gravitational field function can be established according to the following formula:
  • q goal is the virtual optimal pose
  • q is the current random pose
  • d(q,q goal ) is the distance between q and q goal
  • U att (q) is the gravitational field function.
  • the gravitational field function is a piecewise function, when When , the magnitude of gravitational potential energy is proportional to the square of the distance from the current random pose to the virtual optimal pose; and when When , reduce the value of the gravitational calculation function, so as to avoid the problem of excessive gravitational force when the current random pose is far from the virtual optimal pose.
  • Step S1033 Perform iterative optimization on the first random pose according to the gravitational field function to obtain an optimized second random pose.
  • step S1033 may specifically include the following process:
  • Step S10331 taking the first random pose as the current random pose.
  • Step S10332 Calculate the gradient of the current random pose according to the gravitational field function.
  • the gravitational field function can be derived from the distance to obtain the gradient calculation formula shown in the following formula:
  • Step S10333 Perform update calculation on the current random pose according to the gradient of the current random pose to obtain an updated random pose.
  • the current random pose can be updated and calculated according to the following formula:
  • q is the current random pose
  • ⁇ is the preset update step size, and its specific value can be set according to the actual situation
  • q' is the update random pose. Since the gradient direction is the fastest rising direction of the function, the local optimal solution can be obtained through gradient iteration.
  • Step S10334 Determine whether the gradient of the current random pose satisfies a preset iteration termination condition.
  • the iteration termination condition may be: the gradient of the current planned path is less than a preset gradient threshold, or the iterative optimization duration is greater than a preset duration threshold.
  • the specific values of the gradient threshold and the duration threshold can be set according to actual conditions. In this way, the duration of iterative optimization can be controlled, so as to ensure that the overall sampling efficiency will not be affected due to excessive time consumption while expanding in the direction of the gravitational field.
  • step S10335 is performed; if the gradient of the current random pose meets the iteration termination condition, step S10336 is performed.
  • Step S10335 taking the updated random pose as the new current random pose.
  • Step S10336 Determine the updated random pose as the optimized random pose.
  • the optimized random pose is recorded as the second random pose here.
  • Step S104 using the second random pose as a random sampling result in the preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.
  • any manipulator motion planning algorithm in the prior art can be used to plan the motion of the manipulator, including but not limited to specific planning algorithms such as RRT or BiRRT.
  • specific planning algorithms such as RRT or BiRRT.
  • RRT or BiRRT specific planning algorithms
  • the embodiment of the present application optimizes the random sampling process based on the existing robotic arm motion planning algorithm. For each random sampling, the optimized second random bit is used. The original first random pose is used as the random sampling result, and the motion planning of the robotic arm is performed based on this random sampling result.
  • the embodiment of the present application uses the ideal virtual optimal motion planning path as the guiding basis for random sampling, and performs iterative optimization on the original random pose (ie, the first random pose) obtained by random sampling, so as to make it a Traction in the direction of the virtual optimal motion planning path to obtain the optimized random pose (ie the second random pose), the random sampling result obtained in this way has obvious orientation, and the robotic arm is moved based on the random sampling result.
  • Planning can reduce a large number of invalid searches and greatly improve the planning efficiency.
  • FIG. 4 shows a structural diagram of an embodiment of a robotic arm motion planning apparatus provided in an embodiment of the present application.
  • a robotic arm motion planning device may include:
  • a virtual path determination module 401 configured to determine a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose;
  • a random sampling module 402 configured to perform random sampling in the free space of the robotic arm to obtain a first random pose of the robotic arm
  • an iterative optimization module 403, configured to iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;
  • the motion planning module 404 is configured to use the second random pose as a random sampling result in a preset motion planning algorithm of the manipulator to perform motion planning for the manipulator.
  • the iterative optimization module may include:
  • a pose mapping unit configured to map the first random pose onto the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose
  • a gravitational field function establishment unit configured to establish a gravitational field function corresponding to the virtual optimal pose
  • An iterative optimization unit configured to iteratively optimize the first random pose according to the gravitational field function to obtain an optimized second random pose.
  • the iterative optimization unit may include:
  • the current random pose determination subunit is used to use the first random pose as the current random pose
  • a gradient calculation subunit configured to calculate the gradient of the current random pose according to the gravitational field function
  • an update calculation subunit configured to update and calculate the current random pose according to the gradient of the current random pose to obtain an updated random pose
  • the current random pose update subunit is configured to use the updated random pose as a new current random pose if the gradient of the current random pose does not meet the preset iteration termination condition;
  • the second random pose determination subunit is configured to determine the updated random pose as the optimized second random pose if the gradient of the current random pose satisfies the iteration termination condition.
  • the gravitational field function establishment unit is specifically configured to establish the gravitational field function according to the following formula:
  • q goal is the virtual optimal pose
  • q is the current random pose
  • d(q,q goal ) is the distance between q and q goal
  • preset distance threshold is the preset distance threshold
  • U att (q) is the gravitational field function
  • the gradient calculation subunit is specifically configured to calculate the gradient of the current random pose according to the following formula:
  • update calculation subunit is specifically configured to update and calculate the current random pose according to the following formula:
  • q is the current random pose
  • ⁇ is the preset update step size
  • q′ is the updated random pose
  • the virtual path determination module is specifically configured to determine a straight line path connecting the initial pose and the target pose as the virtual optimal motion planning path.
  • FIG. 5 shows a schematic block diagram of a robotic arm provided by an embodiment of the present application. For the convenience of description, only parts related to the embodiment of the present application are shown.
  • the robotic arm 5 of this embodiment includes: a processor 50 , a memory 51 , and a computer program 52 stored in the memory 51 and executable on the processor 50 .
  • the processor 50 executes the computer program 52
  • the steps in each of the above embodiments of the robotic arm motion planning method are implemented, for example, steps S101 to S104 shown in FIG. 1 .
  • the processor 50 executes the computer program 52
  • the functions of the modules/units in each of the foregoing apparatus embodiments such as the functions of the modules 401 to 404 shown in FIG. 4, are implemented.
  • the computer program 52 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 51 and executed by the processor 50 to complete the this application.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the robotic arm 5 .
  • FIG. 5 is only an example of the robot arm 5, and does not constitute a limitation to the robot arm 5. It may include more or less components than the one shown, or combine some components, or different components
  • the robotic arm 5 may further include an input and output device, a network access device, a bus, and the like.
  • the processor 50 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 51 may be an internal storage unit of the robotic arm 5 , such as a hard disk or a memory of the robotic arm 5 .
  • the memory 51 may also be an external storage device of the robotic arm 5, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the robotic arm 5 card, Flash Card, etc.
  • the memory 51 may also include both an internal storage unit of the robotic arm 5 and an external storage device.
  • the memory 51 is used to store the computer program and other programs and data required by the robotic arm 5 .
  • the memory 51 can also be used to temporarily store data that has been output or will be output.
  • the disclosed apparatus/robot and method may be implemented in other ways.
  • the device/manipulator embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) ), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer-readable Storage media exclude electrical carrier signals and telecommunications signals.

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Abstract

La présente demande se rapporte au domaine technique des robots, et concerne en particulier un procédé et un appareil de planification de mouvement de bras robotisé, ainsi qu'un support de stockage lisible par ordinateur et un bras robotisé. Le procédé comprend les étapes consistant à : déterminer un trajet de planification de mouvement optimal virtuel d'un bras robotisé se déplaçant d'une pose initiale prédéfinie vers une pose cible prédéfinie; effectuer un échantillonnage aléatoire dans un espace libre du bras robotisé, de façon à obtenir une première pose aléatoire du bras robotisé; effectuer une optimisation itérative sur la première pose aléatoire selon le trajet de planification de mouvement optimal virtuel, de façon à obtenir une seconde pose aléatoire optimisée; et considérer la seconde pose aléatoire comme résultat d'échantillonnage aléatoire dans un algorithme de planification de mouvement de bras robotisé prédéfini pour effectuer une planification de mouvement sur le bras robotisé. Au moyen de la présente demande, un trajet de planification de mouvement optimal virtuel est considéré comme base de guidage pendant un échantillonnage aléatoire, et une optimisation itérative est effectuée sur une pose aléatoire d'origine obtenue au moyen de l'échantillonnage aléatoire, de telle sorte qu'un résultat d'échantillonnage aléatoire présente une qualité de guidage évidente, et un grand nombre de recherches invalides peut être réduit, ce qui permet d'améliorer considérablement l'efficacité de planification.
PCT/CN2021/124620 2021-03-22 2021-10-19 Procédé et appareil de planification de mouvement de bras robotisé, ainsi que support de stockage lisible et bras robotisé WO2022198994A1 (fr)

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