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WO2023024278A1 - Procédé d'optimisation de pose d'articulation de robot, procédé de commande de robot et robot - Google Patents

Procédé d'optimisation de pose d'articulation de robot, procédé de commande de robot et robot Download PDF

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Publication number
WO2023024278A1
WO2023024278A1 PCT/CN2021/131673 CN2021131673W WO2023024278A1 WO 2023024278 A1 WO2023024278 A1 WO 2023024278A1 CN 2021131673 W CN2021131673 W CN 2021131673W WO 2023024278 A1 WO2023024278 A1 WO 2023024278A1
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Prior art keywords
joint
robot
angular velocity
optimization
optimization function
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PCT/CN2021/131673
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English (en)
Chinese (zh)
Inventor
刘益彰
熊友军
罗璇
张志豪
陈春玉
葛利刚
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Ubtech Robotics Corp
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Ubtech Robotics Corp
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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
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • 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
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
    • 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
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
    • 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
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/1651—Programme controls characterised by the control loop acceleration, rate control
    • 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
    • B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages

Definitions

  • the present application relates to the technical field of robot control, in particular to a robot joint pose optimization method, a robot control method and a robot.
  • Robots generally have multiple degrees of freedom, and through the coordination of multiple degrees of freedom to complete various tasks, in order to increase the range of motion and flexibility of the robot, many robots have added redundant joints, but the processing of redundant joints generally requires Very complex logic processing and classification calculations, which bring many challenges to the control of the robot.
  • some methods such as Newton's method are used to calculate the kinematic inverse solution in motion planning, which not only takes a long time to calculate, but also is difficult to calculate. , which leads to a longer total time for motion planning and lower planning efficiency.
  • the embodiment of the present application provides a robot joint pose optimization method, a robot control method, and a robot.
  • the optimization method uses the idea of double-layer optimization to deal with joint redundancy, and can obtain better joint angles to ensure that the robot can be completed first.
  • the speed task of the joints in the Cartesian space also makes each joint angle have a large range of motion.
  • Embodiments of the present application provide a method for optimizing robot joint poses, including:
  • the first optimization function includes the inequality constraint and the equality constraint including the slack variable that the joint angular velocity satisfies;
  • the second optimization function includes the constraint condition that the joint angular velocity satisfies and contains the same slack variable
  • An optimal joint angle of the robot is obtained according to the optimal joint angular velocity, and a pose of the robot joint is optimized according to the optimal joint angle of the robot.
  • the inequality constraint satisfied by the joint angular velocity is obtained through the following steps, including:
  • a synthetic constraint to obtain the joint angular velocity is constructed as the inequality constraint.
  • the first constraint condition is:
  • i-th joint angle in, and are the upper and lower limits of the i-th joint angle, respectively; is the i-th joint angle at time t; is the joint angular velocity of the i-th joint; T is the control command cycle of the robot.
  • the second constraint is:
  • the first optimization function takes the square of the slack variable as the optimization index
  • w is the slack variable
  • J is the speed Jacobian matrix of the robot
  • J is the joint angular velocity vector of all joints of the robot
  • the second optimization function uses the Euclidean distance between the joint angle and the joint limit as the optimization index
  • q is described Euclidean distance; and are the upper and lower limits of the i-th joint angle, respectively; is the i-th joint angle at time t.
  • the constraints containing the same slack variable of the second optimization function are the same as the equality constraints of the first optimization function, and the second optimization function further includes that the joint angular velocity satisfies The inequality constraints of ;
  • i-th joint angle at time t-1 is the i-th joint angle at time t-1; T is the control instruction period of the robot; w is the relaxation variable, J is the speed Jacobian matrix of the robot, is the joint angular velocity vector of all joints of the robot; is the terminal velocity vector of the robot; and are the upper and lower limits of the i-th joint angle, respectively; and are the upper and lower limits of the i-th joint angular velocity, respectively; and are the upper limit and lower limit of the i-th joint angular acceleration, respectively; and are the i-th joint angular velocity at time t-1 and time t, respectively.
  • Embodiments of the present application also provide a robot control method, including:
  • the robot is controlled to perform corresponding operations according to the optimized joint poses.
  • An embodiment of the present application also provides a robot, the robot includes a processor and a memory, the memory stores a computer program, and the processor is used to execute the computer program to implement the above-mentioned robot joint pose optimization method or robot Control Method.
  • Embodiments of the present application also provide a readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the above-mentioned robot joint pose optimization method or robot control method.
  • the robot joint pose optimization method of the embodiment of the present application adopts the idea of double-layer optimization to deal with joint redundancy, by constructing the first optimization function with the joint angular velocity as the optimization variable, the robot terminal speed as the control target, and the joint angle away from the joint
  • the limit is the second optimization function of the control target.
  • the same slack variable is introduced into the two optimization functions and corresponding constraints are added, and the joint angle is optimally solved by combining the two optimization functions, which can ensure that the obtained solution can Prioritize the completion of the Cartesian space task, and on this basis, leave enough margin for each joint angle, so as to stay away from the joint limit.
  • the joint pose optimization method is not only suitable for joint angle optimization of rotational joint robots, but also suitable for joint position optimization of translational joints and translation-rotation hybrid robots, and has strong universality.
  • FIG. 1 is a schematic diagram of the first flow chart of the robot joint pose optimization method of the embodiment of the present application
  • FIG. 2 is a schematic diagram of the second flow chart of the robot joint pose optimization method of the embodiment of the present application
  • FIG. 3 is a schematic flow diagram of a robot control method in an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a robot joint pose optimization device according to an embodiment of the present application.
  • the quadratic programming problem is mainly the process of selecting the optimal solution from multiple solutions under the constraints of equality and inequality.
  • the main form of the quadratic programming problem is as follows:
  • H is a Hessian matrix
  • x is an n-dimensional optimization variable
  • f is a row vector
  • a eq is an mxn (m ⁇ n) dimensional matrix
  • b eq is m Row and column vector
  • Ax ⁇ b is an inequality constraint
  • A is a matrix with n columns
  • b is a column vector.
  • the range of motion and flexibility of the robot are often increased by adding redundant joints.
  • the existing processing of redundant joints is generally very complicated and takes a long time to calculate, and there are certain limitations in practical use.
  • considering the characteristics of the robot's own structure it often has various constraints such as joint angles, joint angular velocities, and joint torques in different application scenarios.
  • Optimal kinematics inverse solution the embodiment of the present application will use the quadratic programming problem to optimize the solution, specifically, the joint angle is optimized and solved by constructing a double-layer optimization index (ie, a double-layer quadratic optimization problem).
  • the robot joint pose optimization method of the embodiment of the present application constructs the first optimization function with the joint angular velocity as the optimization variable and the robot end speed as the control target, and the joint angular velocity as the optimization variable and the joint angle away from the joint limit as the control target
  • the second optimization function and introduce the same slack variable to these two optimization functions and add corresponding constraints.
  • FIG. 1 is a schematic diagram of a first flowchart of a method for optimizing a joint pose of a robot according to an embodiment of the present application.
  • the robot joint pose optimization method includes:
  • Step S110 constructing a first optimization function with the joint angular velocity as the optimization variable and the robot terminal velocity as the control objective, the first optimization function includes the inequality constraints that the joint angular velocity satisfies and the equality constraints including slack variables.
  • a quadratic programming problem is constructed here with the joint angular velocity as the control variable and the end velocity as the control target, which is the above-mentioned first optimization function. It can be understood that the "first" in the first optimization function is mainly used to distinguish it from other optimization functions mentioned later.
  • the first optimization function will introduce a slack variable, and obtain the index to be optimized based on the slack variable.
  • the optimization index may be the square of the slack variable, etc., of course, may also take other forms related to the slack variable, which are not limited here.
  • the first optimization function will also be added with corresponding constraints, which mainly include equality constraints with slack variables that the joint angular velocity satisfies, and inequality constraints that are directly or indirectly related to the joint angular velocity, so as to ensure that the obtained joint angular velocity
  • constraints which mainly include equality constraints with slack variables that the joint angular velocity satisfies, and inequality constraints that are directly or indirectly related to the joint angular velocity, so as to ensure that the obtained joint angular velocity
  • the optimal solution can achieve the speed index of the terminal under the constraints of each joint.
  • the velocity Jacobian matrix of the robot can reflect the relationship between the angular velocity of each joint and the terminal velocity, in the case of introducing the slack variable, the equality constraint equation satisfied by the joint angular velocity, the slack variable and the terminal velocity will be constructed here.
  • the inequality constraints of the first optimization function may include, but are not limited to, joint angle constraints, physical constraints on joint angular velocity itself, and joint angular acceleration constraints, for example.
  • the inequality constraints satisfied by the joint angular velocity can be obtained through the following steps, including:
  • Step S210 constructing joint angle constraints that each joint of the robot satisfies in a corresponding control period, and converting the joint angle constraints into a first constraint condition with joint angular velocity as a variable.
  • joint limit constraints satisfied by each joint angle of the robot are first constructed, and then converted to obtain the corresponding joint angular velocity constraints.
  • the above-mentioned joint limit mainly refers to the limit of the joint position; and for robots with rotary joints, the above-mentioned joint limit mainly refers to Limits on joint angles.
  • Step S220 construct the joint angular acceleration constraints that each joint satisfies in the corresponding control period, and convert the joint angular acceleration constraints into a second constraint condition with the joint angular velocity as a variable.
  • this embodiment will first construct the constraints that the angular acceleration of each joint of the robot satisfies, and then convert to obtain the corresponding joint angular velocity constraint.
  • Step S230 based on the first constraint condition, the second constraint condition and the self-constraint of the joint angular velocity, construct a composite constraint to obtain the joint angular velocity as the above-mentioned inequality constraint.
  • the robot also has joint angular velocity constraints, assuming that the upper limit of the i-th joint angular velocity is The lower limit is Then, the self-constraint of joint angular velocity can be expressed as:
  • joint angle (or joint position) constraint joint angular velocity constraint and joint angular acceleration constraint to obtain the composite inequality constraint of joint angular velocity is only an example of adding inequality, and in actual application, you can also add more There are many constraints, which are not limited here.
  • the first optimization function if the square of the slack variable is used as the optimization index, and the corresponding equality constraint equation and inequality constraint are constructed, the first optimization function can be written as follows:
  • Step S120 constructing a second optimization function with the joint angular velocity as the optimization variable and the robot's joint angle away from the joint limit as the control objective, the second optimization function includes the constraint conditions with the same slack variable that the joint angular velocity satisfies.
  • another quadratic programming problem that is, the above-mentioned second optimization function
  • the second optimization function uses the Euclidean distance between the joint angle and the joint limit as the optimization index.
  • Euclidean distance In addition to the Euclidean distance, other calculation formulas that can reflect the joint angle and the joint limit can also be used , is not limited here.
  • q is the Euclidean distance; and are the upper and lower limits of the i-th joint angle, respectively; is the i-th joint angle at time t.
  • a double-layer quadratic programming problem is used to comprehensively solve an optimal joint angular velocity.
  • the joint angular velocity of the second optimization function in order to make the joint angular velocity finally solved under the premise of ensuring the end velocity of the Cartesian space control, further make each joint If the angle gradually approaches the optimal solution, the joint angular velocity of the second optimization function also needs to add corresponding constraints.
  • the joint angular velocity of the second optimization function may add an equality constraint including the same slack variable and an inequality constraint related to the joint angular velocity.
  • the second optimization function if the optimization index of each joint angle is farthest from the joint limit as an example, and the same equality constraint equation and inequality constraint as the first optimization function are added, the second optimization function can be written as In the following form:
  • step S130 the solution of the slack variable is obtained by solving according to the first optimization function, and the optimal joint angular velocity is obtained according to the solution of the slack variable and the second optimization function.
  • an open-source solver can be used to optimally solve the first optimization function to obtain the joint angular velocity and the solution of the slack variable; then, use the solved slack variable
  • the solution of the second optimization function is optimally solved, and the optimal joint angular velocity can be obtained at this time, which is the required joint angular velocity.
  • step S140 the optimal joint angle of the robot is obtained according to the optimal joint angular velocity, and the pose of the robot joint is optimized according to the optimal joint angle of the robot.
  • the optimal joint angle can be obtained through an integral operation. Furthermore, the posture (such as angle or position) of the robot joints can be adjusted according to the optimal joint angles, so as to ensure that the robot can perform corresponding task operations.
  • the joint pose optimization method of the robot in this embodiment uses the idea of double-layer optimization to deal with the problem of joint redundancy, by constructing the first optimization function that takes the joint angular velocity as the optimization variable, the robot end speed as the control target and introduces the slack variable, and The second optimization function that takes the joint angle away from the joint limit as the control target and introduces the same slack variable, and adds synthetic constraints such as the angle, angular velocity, and angular acceleration of each joint to these two optimization functions, where the second optimization
  • the function is further solved for joint angular velocity within the range of joint angle constraints satisfied by the first optimization function, so it can be realized that under various joint constraint conditions, the velocity task in Cartesian space can be completed first, and on this basis, each joint angle can be given Sufficient margin is left, that is, it has a large activity margin.
  • the method is versatile and can be used to automatically optimize the joint positions or joint angles of various types of robots such as rotational joints, translational joints, and translational-rotation
  • this embodiment proposes a robot control method.
  • -Robots and the like in mixed motion are not limited here.
  • the rotating joint type robot means that the robot drives the robot joints connected to the steering gear through the steering gear outputting the rotation angle to realize the movement corresponding to the rotation angle.
  • the robot of the translational joint type means that the robot drives the robot joints connected to the steering gear through the steering gear outputting the linear displacement to realize the movement corresponding to the translation angle.
  • the robot has redundant joints, and various tasks can be completed by coordinating these redundant joints.
  • the robot control method includes:
  • Step S310 acquiring state parameters related to joint angular velocity of the robot in the current control command cycle.
  • Step S320 using the robot joint pose optimization method to optimize the joint pose of the next control command cycle according to the state parameters.
  • state parameters need to be obtained can be determined according to the constructed first optimization function and the second optimization function. These state parameters can be directly collected by sensors, etc., or can be obtained through calculation and processing of collected data. What is obtained later is not limited here.
  • the joint angle corresponding to the control command cycle, the expected terminal velocity, and the Jacobian matrix can be obtained, and then the known quantities are substituted into the optimization function to be used to calculate the joint angular velocity.
  • the optimization variable and the slack variable are solved, and the second optimization function is the same.
  • Step S330 controlling the robot to perform corresponding operations according to the optimized joint poses.
  • these joint angles or joint positions required for the next control command cycle of the robot are obtained, these joint angles or joint positions are sent as commands to the corresponding joint motors, so that the robot performs corresponding operations, for example, can It is robot obstacle avoidance task, human-computer interaction task, etc.
  • the robot control method of this embodiment uses the robot joint pose optimization method to automatically optimize the joint pose, so that the robot joint position can always have the largest activity margin under the premise of ensuring the Cartesian space pose. .
  • the robot joint pose optimization device 100 includes:
  • the construction module 110 is used to construct a first optimization function with the joint angular velocity as the optimization variable and the robot terminal velocity as the control target, the first optimization function includes the inequality constraint and the equality constraint including the slack variable that the joint angular velocity satisfies.
  • the construction module 110 is also used to construct a second optimization function with the joint angular velocity as the optimization variable and the joint angle of the robot away from the joint limit as the control target, the second optimization function includes the same slack that the joint angular velocity satisfies Variable constraints.
  • the solving module 120 is configured to solve according to the first optimization function to obtain the solution of the slack variable, and obtain the optimal joint angular velocity according to the solution of the slack variable and the second optimization function.
  • the solving module 120 is also used to obtain the optimal joint angle of the robot according to the optimal joint angular velocity.
  • An optimization module 130 configured to optimize the poses of the robot joints according to the optimal joint angles of the robot.
  • the device in this embodiment corresponds to the method in the above-mentioned embodiment 1, and the optional items in the above-mentioned embodiment 1 are also applicable to this embodiment, so the description will not be repeated here.
  • the present application also provides a robot.
  • the robot includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program so that the robot performs the above-mentioned robot joint pose optimization method Or the functions of each module in the above-mentioned robot joint pose optimization device.
  • the present application also provides a readable storage medium for storing the computer program used in the above robot.
  • each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flow diagrams, and combinations of blocks in the block diagrams and/or flow diagrams can be implemented by a dedicated hardware-based system that performs the specified function or action. may be implemented, or may be implemented by a combination of special purpose hardware and computer instructions.
  • each functional module or unit in each embodiment of the present application may be integrated to form an independent part, each module may exist independently, or two or more modules may be integrated to form an independent part.
  • the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. .

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Software Systems (AREA)
  • Manipulator (AREA)

Abstract

Un procédé d'optimisation de pose d'articulation de robot, un procédé de commande de robot et un robot sont divulgués. Le procédé consiste à : élaborer une première fonction d'optimisation à l'aide d'une vitesse angulaire d'articulation utilisée en tant que variable d'optimisation et d'une vitesse de terminal de robot utilisée en tant que cible de commande, la première fonction d'optimisation comprenant une contrainte d'inégalité satisfaite par la vitesse angulaire d'articulation et une contrainte d'égalité comprenant une variable d'écart ; élaborer une seconde fonction d'optimisation à l'aide de la vitesse angulaire d'articulation utilisée en tant que variable d'optimisation et d'un angle d'articulation à l'opposé d'une limite d'articulation utilisée en tant que cible de commande, la seconde fonction d'optimisation comprenant des conditions de contrainte comprenant la même variable d'écart satisfaites par la vitesse angulaire d'articulation ; et résoudre la première fonction d'optimisation pour obtenir une solution pour la variable d'écart, et utiliser la solution de la variable d'écart dans la seconde fonction d'optimisation pour obtenir une vitesse angulaire d'articulation optimale, ce qui permet une résolution pour obtenir un angle d'articulation optimal pour optimiser la pose d'articulation du robot. Le présent procédé peut obtenir une solution optimale pour une pose d'articulation, amenant chaque angle d'articulation à présenter de manière cohérente une marge d'activité plus grande tout en garantissant la priorité de la vitesse d'achèvement de tâche.
PCT/CN2021/131673 2021-08-24 2021-11-19 Procédé d'optimisation de pose d'articulation de robot, procédé de commande de robot et robot Ceased WO2023024278A1 (fr)

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