Motion planning method and system for two-wheeled mobile robotic arm based on iterative learning network

Abstract

The present application discloses a motion planning method and system for a two-wheeled mobile manipulator based on an iterative learning network. The method comprises the following steps: obtaining a kinematic model of a mobile platform; obtaining a homogeneous transformation matrix of the manipulator joints and determining the pose vector of the manipulator's end effector; determining the inverse kinematics equation of the manipulator based on the pose vector, the joint angular velocity of the manipulator, and the angular velocity of the left and right driving wheels of the mobile platform; determining a manipulator motion planning model based on quadratic programming based on the kinematic model, the inverse kinematics equation, and a repeatable target task; solving the manipulator motion planning model using an iterative learning network solution algorithm, obtaining the angular velocity of each joint of the manipulator and the angular velocity of the left and right driving wheels of the mobile platform, and transmitting the results to a lower computer controller. The present application enables the manipulator to fully track the target trajectory in an infinite time motion planning scheme and can be widely used in the field of manipulator control technology.

Description

Technical Field

[0001] The present application relates to the field of robotic arm control technology, and in particular to a motion planning method and system for a two-wheeled mobile robotic arm based on an iterative learning network. Background Art

[0002] With the continuous advancement of artificial intelligence and computer technology, the application areas and scope of robotic systems are continuously expanding, and robotic arm systems are becoming increasingly integrated into our work and daily lives. Robotic arm systems play a vital role in various fields, including home life, industrial manufacturing, the power industry, and national defense. Furthermore, to increase flexibility, mobile robotic arms offer larger workspaces and more flexible control methods compared to fixed robotic arms, allowing them to be used in more complex working environments. Among existing robotic motion planning algorithms, quadratic programming-based solutions have become a popular choice due to their advantages, such as flexible design of parameters and the inclusion of various forms of constraints. Traditional solutions for quadratic programming-based motion planning are mostly based on infinite time methods, meaning that only when time approaches infinity can the mobile arm theoretically fully track the target trajectory. However, for a mobile robotic arm operating within a finite time, motion planning solutions based on infinite time cannot fully track the target trajectory. Summary of the Invention

[0003] In view of this, the present application provides a two-wheeled mobile robot arm motion planning method and system based on an iterative learning network, so that the robot arm can fully track the target trajectory in an infinite time motion planning scheme.

[0004] One aspect of the present application provides a motion planning method for a two-wheeled mobile manipulator based on an iterative learning network, comprising:

[0005] Obtaining a kinematic model of the mobile platform according to structural parameters of the dual-wheel differentially driven mobile platform;

[0006] According to the structural parameters of the robotic arm, a homogeneous transformation matrix of the joints of the robotic arm is obtained; according to the homogeneous transformation matrix, a pose vector of the end effector of the robotic arm is determined; according to the pose vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform, an inverse kinematics equation of the robotic arm is determined; the robotic arm is set on the mobile platform;

[0007] Determining a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation, and a repeatable target task;

[0008] Determining an iterative learning network solution algorithm, and using the iterative learning network solution algorithm to solve the robotic arm motion planning model to obtain the angular velocity of each joint of the robotic arm and the angular velocity of the left and right drive wheels of the mobile platform;

[0009] The angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform are transmitted to the lower computer controller so that the lower computer controller can drive the joints of the robotic arm and the mobile platform to move, and make the end effector of the robotic arm track the target trajectory in the robotic arm motion planning model.

[0010] Optionally, obtaining the kinematic model of the mobile platform according to the structural parameters of the dual-wheel differentially driven mobile platform includes:

[0011] Determine a world coordinate system according to the geometric model of the mobile platform, wherein the world coordinate system is the working coordinate system of the robotic arm;

[0012] The kinematic model of the mobile platform is determined in the world coordinate system as follows:

[0013]

[0014]

[0015] in and are the velocity components of the mobile platform in the X, Y and Z axis directions in the world coordinate system respectively; is the angular velocity of the mobile platform; r is the radius of the left and right driving wheels of the mobile platform, v l , v r are the forward speeds of the left and right driving wheels respectively;

[0016] Integrating the kinematic model to obtain a position of a mounting point of the robotic arm on the mobile platform in the world coordinate system and a heading angle of the mobile platform;

[0017]

[0018]

[0019] Wherein x, y and z are the positions of the mounting point in the X, Y and Z axis directions in the world coordinate system respectively; φ is the heading angle of the mobile platform.

[0020] Optionally, the robotic arm is a six-degree-of-freedom robotic arm, and obtaining a homogeneous transformation matrix of the robotic arm joints according to the structural parameters of the robotic arm includes:

[0021] Establish a robot coordinate system with the forward direction of the mobile platform as the positive direction of the X axis, the line between the left and right drive wheels as the Y axis, and the installation point as the origin;

[0022] Obtain the homogeneous transformation matrices corresponding to the six joints of the robotic arm respectively through the DH modeling method;

[0023] The homogeneous transformation matrices corresponding to the six joints of the robotic arm are converted from the world coordinate system to the robot coordinate system to obtain the comprehensive homogeneous transformation matrix as follows:

[0024]

[0025] Where T W represents the comprehensive homogeneous transformation matrix, T1, T2, T3, T4, T5, and T6 represent the homogeneous transformation matrices corresponding to the six joints of the robotic arm respectively;

[0026] Determining the pose vector of the end effector of the robotic arm according to the homogeneous transformation matrix includes:

[0027] The pose vector of the end effector of the robotic arm is calculated based on the comprehensive homogeneous transformation matrix and the homogeneous transformation matrices corresponding to the six joints of the robotic arm as follows:

[0028]

[0029] in A pose vector representing the end effector of the robotic arm;

[0030] Determining the inverse kinematics equation of the robotic arm according to the posture vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform includes:

[0031] The Jacobian matrix of the end effector is obtained as follows:

[0032]

[0033] in The vector consisting of the angles of the six joints and the rotation angles of the left and right driving wheels is used as the first vector, and the time derivative of the first vector is defined as A vector consisting of the rotational angular velocities of the six joints and the rotational angular velocities of the left and right driving wheels is used as a second vector;

[0034] The inverse kinematics equation of the robotic arm is determined as follows:

[0035]

[0036] in is the derivative of the pose vector.

[0037] Optionally, determining a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation, and a repeatable target task includes:

[0038] Using the repeated motion index as the optimization index, under the constraints of the inverse kinematics equation, the first motion planning model is determined as follows:

[0039]

[0040]

[0041] in express The second norm of represents a coefficient vector;

[0042] The penalty function method is used to convert the first motion planning model with equality constraints into the second motion planning model without constraints as follows:

[0043]

[0044] Where σ>>0 represents the penalty factor, represents the vth row of the Jacobian matrix J(t), Represents a vector The vth row of .

[0045] Optionally, determining an iterative learning network solution algorithm includes:

[0046] The partial derivatives of the unconstrained second motion planning model with respect to the control variables are calculated as follows:

[0047]

[0048] where i∈Z + represents the number of iterations of the target task, the time derivative As the control variable, the control variable is expressed as is the pose vector actually measured by the end effector;

[0049] The tracking errors generated when all follower manipulators track the target trajectory of the leader manipulator are calculated according to the error calculation expression, wherein the manipulators include one leader manipulator and multiple follower manipulators. The error calculation expression is as follows:

[0050]

[0051] The iterative learning network solution algorithm is determined as follows:

[0052]

[0053] Where h represents the learning step size, represents the input of iterative learning, The update law is determined as:

[0054]

[0055] in Input gain matrix for the iteration.

[0056] Optionally, the transmitting of the angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller includes:

[0057] Integrating the angular velocities of the joints of the robotic arm and the angular velocities of the left and right driving wheels of the mobile platform to obtain the optimal joint angular velocities and optimal left and right driving wheel angular velocities that best match the target task;

[0058] The optimal joint angular velocity and the optimal left and right driving wheel angular velocity are transmitted to the lower computer controller.

[0059] Another aspect of the present application further provides a two-wheeled mobile manipulator motion planning system based on an iterative learning network, comprising:

[0060] The first module is used to obtain a kinematic model of the mobile platform according to the structural parameters of the two-wheel differential drive mobile platform;

[0061] The second module is configured to obtain a homogeneous transformation matrix of the joints of the robotic arm based on the structural parameters of the robotic arm; determine a pose vector of the end effector of the robotic arm based on the homogeneous transformation matrix; and determine an inverse kinematics equation of the robotic arm based on the pose vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform; the robotic arm is disposed on the mobile platform;

[0062] A third module is used to determine a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation and a repeatable target task;

[0063] A fourth module is configured to determine an iterative learning network solution algorithm and use the iterative learning network solution algorithm to solve the robotic arm motion planning model to obtain the angular velocity of each joint of the robotic arm and the angular velocity of the left and right drive wheels of the mobile platform;

[0064] The fifth module is used to transmit the angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller, so that the lower computer controller can drive the joints of the robotic arm and the mobile platform to move, and make the end effector of the robotic arm track the target trajectory in the robotic arm motion planning model.

[0065] Another aspect of the present application further provides a two-wheeled mobile robotic arm, comprising:

[0066] Two-wheel differential-driven mobile platform and robotic arm;

[0067] The two-wheel mobile robotic arm is used to respond to the driving operation of the lower computer controller so that the end effector of the robotic arm tracks the target trajectory in the robotic arm motion planning model;

[0068] The driving operation of the lower computer controller is determined according to the aforementioned two-wheel mobile manipulator motion planning method based on an iterative learning network.

[0069] Another aspect of the present application further provides an electronic device, comprising a processor and a memory;

[0070] The memory is used to store programs;

[0071] The processor executes the program to implement the aforementioned method.

[0072] Another aspect of the present application provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the aforementioned method.

[0073] The present application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, so that the electronic device performs the aforementioned method.

[0074] This application has at least the following beneficial effects:

[0075] For a mobile robotic arm working within a finite time, this application adopts a motion planning scheme based on infinite time. Taking into account that the target tasks of the robotic arm are relatively repeatable, an iterative learning network solution algorithm that can achieve finite time tracking for a dynamic system with repeatable motion is determined, and applied to the motion planning of the mobile robotic arm, realizing the motion planning of the mobile robotic arm within a finite time, so that the robotic arm can track the target trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0077] Figure 1 A schematic flow chart of a motion planning method for a two-wheeled mobile manipulator based on an iterative learning network provided in an embodiment of the present application;

[0078] Figure 2 An example flow chart of a motion planning method for a two-wheeled mobile manipulator based on an iterative learning network provided in an embodiment of the present application;

[0079] Figure 3 A geometric model diagram of a two-wheeled mobile platform provided in an embodiment of the present application;

[0080] Figure 4 This is a trajectory tracking result diagram of a two-wheeled mobile robotic arm provided in an embodiment of the present application;

[0081] Figure 5 A position tracking error diagram of an end effector provided in an embodiment of the present application;

[0082] Figure 6 This is a structural block diagram of a two-wheeled mobile manipulator motion planning device based on an iterative learning network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0084] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0085] The terms "first," "second," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. Furthermore, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0087] Reference Figure 1 The embodiment of the present application provides a motion planning method for a two-wheeled mobile manipulator based on an iterative learning network, including steps S100 to S140, as follows:

[0088] S100: Obtaining a kinematic model of a mobile platform according to structural parameters of a two-wheel differentially driven mobile platform.

[0089] Specifically, the mobile platform can carry a robotic arm and move.

[0090] Furthermore, S100 may include:

[0091] Determine a world coordinate system according to the geometric model of the mobile platform, wherein the world coordinate system is the working coordinate system of the robotic arm;

[0092] The kinematic model of the mobile platform is determined in the world coordinate system as follows:

[0093]

[0094]

[0095] in and are the velocity components of the mobile platform in the X, Y and Z axis directions in the world coordinate system respectively; is the angular velocity of the mobile platform; r is the radius of the left and right driving wheels of the mobile platform, v l , v r are the forward speeds of the left and right driving wheels respectively;

[0096] Integrating the kinematic model to obtain a position of a mounting point of the robotic arm on the mobile platform in the world coordinate system and a heading angle of the mobile platform;

[0097]

[0098]

[0099] Wherein x, y and z are the positions of the mounting point in the X, Y and Z axis directions in the world coordinate system respectively; φ is the heading angle of the mobile platform.

[0100] S110: According to the structural parameters of the robotic arm, the homogeneous transformation matrix of the joints of the robotic arm is obtained; according to the homogeneous transformation matrix, the posture vector of the end effector of the robotic arm is determined; according to the posture vector, the joint angular velocity of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform, the inverse kinematics equation of the robotic arm is determined; the robotic arm is set on the mobile platform.

[0101] Specifically, the robotic arm of this embodiment may include multiple joints, such as four joints, five joints, or six joints. For example, the embodiment of the present application is described using a robotic arm with six joints as an example.

[0102] Therefore, the step of obtaining the homogeneous transformation matrix of the joints of the robotic arm according to the structural parameters of the robotic arm in S110 may further include S111 to S113:

[0103] S111: Establish a robot coordinate system with the forward direction of the mobile platform as the positive direction of the X axis, the line between the left and right driving wheels as the Y axis, and the installation point as the origin.

[0104] S112: Obtain the homogeneous transformation matrices corresponding to the six joints of the robotic arm respectively through the DH modeling method.

[0105] S113: Convert the homogeneous transformation matrices corresponding to the six joints of the robotic arm from the world coordinate system to the robot coordinate system to obtain a comprehensive homogeneous transformation matrix as follows:

[0106]

[0107] Where T W represents the comprehensive homogeneous transformation matrix, and T1, T2, T3, T4, T5, and T6 respectively represent the homogeneous transformation matrices corresponding to the six joints of the robotic arm.

[0108] The step of determining the pose vector of the end effector of the robotic arm according to the homogeneous transformation matrix in S110 may further include S114:

[0109] S114: The pose vector of the end effector of the robotic arm is calculated based on the comprehensive homogeneous transformation matrix and the homogeneous transformation matrices corresponding to the six joints of the robotic arm as follows:

[0110]

[0111] in represents the pose vector of the end effector of the robotic arm.

[0112] The step of determining the inverse kinematics equation of the manipulator according to the posture vector, the joint angular velocity of the manipulator, and the angular velocities of the left and right driving wheels of the mobile platform in S110 may further include S115 to S116:

[0113] S115: Obtain the Jacobian matrix of the end effector as follows:

[0114]

[0115] in The vector consisting of the angles of the six joints and the rotation angles of the left and right driving wheels is used as the first vector, and the time derivative of the first vector is defined as A vector representing the rotational angular velocities of the six joints and the rotational angular velocities of the left and right driving wheels is used as the second vector.

[0116] S116: Determine the inverse kinematics equation of the robotic arm as follows:

[0117]

[0118] in is the derivative of the pose vector.

[0119] S120: Determine a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation, and a repeatable target task.

[0120] Specifically, the target task of this embodiment is a task performed by a robotic arm, and the target task can be repeatedly performed multiple times.

[0121] Furthermore, S120 may include S121 to S122:

[0122] S121: Using the repeated motion index as the optimization index, under the constraints of the inverse kinematics equation, determine the first motion planning model as follows:

[0123]

[0124]

[0125] in express The second norm of represents a coefficient vector.

[0126] S122: Using a penalty function method, the first motion planning model with equality constraints is converted into a second motion planning model without constraints as follows:

[0127]

[0128] Where σ>>0 represents the penalty factor, represents the vth row of the Jacobian matrix J(t), Represents a vector The vth row of .

[0129] S130: Determine an iterative learning network solution algorithm, and use the iterative learning network solution algorithm to solve the robot arm motion planning model to obtain the angular velocity of each joint of the robot arm and the angular velocity of the left and right driving wheels of the mobile platform.

[0130] Specifically, in this embodiment, the angular velocity of each joint of the robotic arm can be used as the control quantity of each joint, and the angular velocity of the left and right driving wheels of the mobile platform can be used as the control quantity of the corresponding driving wheels.

[0131] Furthermore, the step of determining the iterative learning network solution algorithm in S130 may include S131 to S133:

[0132] S131: Calculate and obtain the partial derivative of the unconstrained second motion planning model with respect to the control variable as follows:

[0133]

[0134] where i∈Z + represents the number of iterations of the target task, the time derivative As the control variable, the control variable is expressed as is the pose vector actually measured by the end effector.

[0135] S132: Calculate the tracking errors generated when all follower manipulators track the target trajectory of the leader manipulator according to an error calculation expression, wherein the manipulators include one leader manipulator and multiple follower manipulators. The error calculation expression is as follows:

[0136]

[0137] S133: Determine the iterative learning network solution algorithm as follows:

[0138]

[0139] Where h represents the learning step size, represents the input of iterative learning, The update law is determined as:

[0140]

[0141] in Input gain matrix for the iteration.

[0142] S140: Transmit the angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller, so that the lower computer controller drives the joints of the robotic arm and the mobile platform to move, and enables the end effector of the robotic arm to track the target trajectory in the robotic arm motion planning model.

[0143] Furthermore, S140 may include S141 to S142:

[0144] S141: Integrate the angular velocities of the joints of the robotic arm and the angular velocities of the left and right driving wheels of the mobile platform to obtain the optimal joint angular velocities and optimal left and right driving wheel angular velocities that best match the target task.

[0145] S142: Transmitting the optimal joint angular velocity and the optimal left and right driving wheel angular velocities to the lower computer controller.

[0146] In order to facilitate a clearer understanding of the method provided by this application, the method of this application will be explained below with a complete optional example.

[0147] Specifically, refer to Figure 2 This embodiment may include S1 to S6, which are specifically as follows:

[0148] S1: Based on the structural parameters of the two-wheel differentially driven mobile platform, the kinematic model of the two-wheel platform of the mobile manipulator is obtained.

[0149] Specifically, step S1 may include:

[0150] For a two-wheeled mobile robot, the mounting point of the robot is at the midpoint P of the line connecting the left and right drive wheels. Therefore, based on the geometric model of the two-wheeled mobile platform, the following two coordinate systems are first defined:

[0151] 1) World coordinate system: The working coordinate system of the two-wheeled mobile robot system, with the origin of the coordinate system being W.

[0152] 2) Robot coordinate system: This coordinate system is a dynamic coordinate system. Its origin is the installation point of the robot arm, which is also the midpoint of the line connecting the left and right drive wheels, that is, point P.p The positive direction of the axis is always the forward direction of the mobile platform, p The positive direction of the shaft is the direction of the line connecting the two driving wheels.

[0153] First, in the world coordinate system W, the kinematic model of the two-wheel differential drive mobile platform can be obtained:

[0154]

[0155]

[0156] in, and are the velocity components of the mobile platform in the X, Y and Z axis directions in the world coordinate system respectively; is the angular velocity of the mobile platform. r is the radius of the left and right driving wheels, v l , v r are the forward speeds of the left and right drive wheels respectively. By integrating the above two equations, we can obtain the position and heading angle of the robot arm installation point P in the world coordinate system, as follows:

[0157]

[0158]

[0159] Where x, y, and z are the positions of the robot arm installation point on the two-wheeled mobile platform in the X, Y, and Z axis directions in the world coordinate system respectively; φ is the heading angle of the two-wheeled mobile platform.

[0160] S2: Based on the structural parameters of the six-degree-of-freedom robotic arm carried on the mobile platform, the homogeneous transformation matrix of the six joints of the robotic arm is obtained, and the kinematic equations of the robotic arm end effector pose vector, joint angular velocity, and angular velocity of the left and right wheels of the mobile platform are further established.

[0161] Specifically, step S2 may include:

[0162] According to the structural parameters of the six-axis robotic arm carried by the mobile platform, the pose vector of the end effector is calculated:

[0163]

[0164] in is the pose vector of the end effector of the two-wheeled mobile robot in the world coordinate system, T1, T2, T3, T4, T5, T6 are the homogeneous transformation matrices corresponding to the robot joints 1-6, and they can be obtained by the DH modeling method, P E represents the pose vector of the end effector in the end coordinate system of the manipulator, T WThe homogeneous transformation matrix from the robot coordinate system to the world coordinate system is defined as follows:

[0165]

[0166] Further obtain the Jacobian matrix of the end effector:

[0167]

[0168] in Represents a vector consisting of 6 joint angles and left and right wheel rotation angles, and defines its time derivative represents the vector consisting of the angular velocities of the six joints and the angular velocities of the left and right wheels. Furthermore, the inverse kinematics equation of the two-wheeled mobile manipulator is:

[0169]

[0170] in is the Jacobian matrix, is the pose vector of the end effector of the robotic arm.

[0171] S3: Based on the actual work objectives and the repeatable characteristics of the target tasks, a motion planning scheme for the two-wheeled mobile robot arm based on quadratic programming is developed.

[0172] Specifically, step S3 may include:

[0173] Due to the repeatability of the target task of the two-wheeled mobile manipulator, the repeated motion index is used as the optimization index of the quadratic programming problem. Considering the constraints of the inverse kinematic equation, the motion planning scheme based on the quadratic programming problem is formulated as follows:

[0174]

[0175]

[0176] in Represents the control vector The second norm of Represents a coefficient vector. Using the penalty function method, the above quadratic programming problem with equality constraints can be equivalent to the following unconstrained optimization problem as follows:

[0177]

[0178] Where σ>>0 represents the penalty factor, represents the vth row of the matrix J(t), Represents a vector The vth row of .

[0179] S4: Design an iterative learning network solution algorithm to solve the quadratic programming-based motion planning scheme for the two-wheeled mobile robotic arm designed in S3.

[0180] Specifically, step S4 may include S4.1 to S4.5:

[0181] S4.1. First, use the variable i∈Z + To describe the number of task repetitions of the redundant manipulator system, that is, the number of iterations, the control variable Depend on replace, is the actual pose vector of the end effector;

[0182] S4.2. Calculate the unconstrained optimization objective function Partial derivatives of :

[0183]

[0184] in Represents the unconstrained optimization objective function vector The partial derivative of .

[0185] S4.3. Calculate the target trajectory tracking error of all follower manipulators tracking the leader manipulator: in is the pose vector actually measured by the end effector.

[0186] S4.5. Design an iterative learning network solution algorithm:

[0187]

[0188] Where h represents the learning step size, the input of iterative learning The update law is designed as:

[0189]

[0190] in Input gain matrix for the iteration.

[0191] S5: The obtained joint angular velocity control values ​​of each manipulator and the angular velocity of the left and right wheels of the mobile platform are transmitted to the lower computer controller. The lower computer controller drives the movement of each manipulator joint and the mobile platform so that the end effector of the mobile manipulator can track the target trajectory.

[0192] Specifically, step S5 may include:

[0193] The optimal joint angular velocity of the manipulator and the angular velocity of the left and right driving wheels of the two-wheeled mobile manipulator system obtained by the iterative learning network Again Integrate to obtain the optimal joint angle of the robot arm and the rotation angle of the left and right drive wheels The information is then transmitted to the lower controller to achieve the control goal, so that the end effector of the two-wheeled mobile robot can completely track the given target trajectory.

[0194] In order to verify the effectiveness of this application, this embodiment also conducted a simulation experiment, which is as follows:

[0195] A simulation experiment was conducted using a two-wheel mobile manipulator consisting of a two-wheel differential drive mobile platform and a six-axis manipulator to verify the effectiveness of the two-wheel mobile manipulator motion planning method and system based on iterative learning network provided in this embodiment. The geometric model of the two-wheel mobile platform is as follows: Figure 3 As shown in the figure, the trajectory tracking results of the two-wheel mobile manipulator are as follows: Figure 4 As shown, the target trajectory is a Lissajous figure. Figure 4 The actual trajectory of the end effector, the robot arm, and the two-wheeled mobile platform in completing the trajectory tracking is shown in FIG. Figure 4 The robot end effector trajectory marked in refers to the trajectory of the end effector of the robotic arm. The position tracking error of the end effector is as follows: Figure 5 As shown, The simulation results can verify the effectiveness of this application in solving the motion planning problem of a two-wheeled mobile robotic arm.

[0196] Reference Figure 6 , an embodiment of the present application provides a two-wheeled mobile manipulator motion planning system based on an iterative learning network, comprising:

[0197] The first module is used to obtain a kinematic model of the mobile platform according to the structural parameters of the two-wheel differential drive mobile platform;

[0198] The second module is configured to obtain a homogeneous transformation matrix of the joints of the robotic arm based on the structural parameters of the robotic arm; determine a pose vector of the end effector of the robotic arm based on the homogeneous transformation matrix; and determine an inverse kinematics equation of the robotic arm based on the pose vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform; the robotic arm is disposed on the mobile platform;

[0199] A third module is used to determine a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation and a repeatable target task;

[0200] A fourth module is configured to determine an iterative learning network solution algorithm and use the iterative learning network solution algorithm to solve the robotic arm motion planning model to obtain the angular velocity of each joint of the robotic arm and the angular velocity of the left and right drive wheels of the mobile platform;

[0201] The fifth module is used to transmit the angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller, so that the lower computer controller can drive the joints of the robotic arm and the mobile platform to move, and make the end effector of the robotic arm track the target trajectory in the robotic arm motion planning model.

[0202] The specific implementation of the motion planning system is basically the same as the specific embodiment of the above-mentioned motion planning method, and will not be repeated here.

[0203] The embodiment of the present application further provides a two-wheel mobile robotic arm, comprising: a two-wheel differentially driven mobile platform and a robotic arm;

[0204] The two-wheel mobile robotic arm is used to respond to the driving operation of the lower computer controller so that the end effector of the robotic arm tracks the target trajectory in the robotic arm motion planning model;

[0205] The driving operation of the lower computer controller is determined according to the aforementioned two-wheel mobile manipulator motion planning method based on an iterative learning network.

[0206] An embodiment of the present application further provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned motion planning method when executing the computer program.

[0207] Specifically, the electronic device may be a user terminal or a server.

[0208] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned motion planning method is implemented.

[0209] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0210] The present application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs Figure 1 The method shown.

[0211] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0212] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0213] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0214] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0215] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0216] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0217] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0218] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0219] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A motion planning method for a two-wheeled mobile manipulator based on an iterative learning network, characterized in that: include: Obtaining a kinematic model of the mobile platform according to structural parameters of the dual-wheel differentially driven mobile platform; Obtaining a homogeneous transformation matrix of the joints of the robotic arm according to the structural parameters of the robotic arm; Determining a pose vector of an end effector of the robotic arm according to the homogeneous transformation matrix; determining an inverse kinematics equation of the robotic arm according to the pose vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform; the robotic arm is disposed on the mobile platform; Determining a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation, and a repeatable target task; Determining an iterative learning network solution algorithm, and using the iterative learning network solution algorithm to solve the robotic arm motion planning model to obtain the angular velocity of each joint of the robotic arm and the angular velocity of the left and right drive wheels of the mobile platform; The angular velocity of each joint of the manipulator and the angular velocity of the left and right driving wheels of the mobile platform are transmitted to the lower computer controller, so that the lower computer controller drives the joints of the manipulator and the mobile platform to move, and makes the end effector of the manipulator track the target trajectory in the manipulator motion planning model; The step of determining the iterative learning network solution algorithm comprises the following steps: Obtaining the Jacobian matrix of the end effector; Obtaining a vector consisting of the rotational angular velocities of the six joints of the robotic arm and the rotational angular velocities of the left and right driving wheels; Using a repeated motion index as an optimization index, and under the constraints of the inverse kinematics equation, determining a first motion planning model; Converting the first motion planning model with equality constraints into a second motion planning model without constraints by using a penalty function method; Calculating and obtaining the partial derivative of the unconstrained second motion planning model with respect to the control variable; The iterative learning network solution algorithm is determined according to the Jacobian matrix, the vector, and the partial derivative.

2. The motion planning method for a two-wheeled mobile manipulator based on an iterative learning network according to claim 1, characterized in that: The kinematic model of the mobile platform is obtained according to the structural parameters of the mobile platform with two-wheel differential drive, including: Determine a world coordinate system according to the geometric model of the mobile platform, wherein the world coordinate system is the working coordinate system of the robotic arm; The kinematic model of the mobile platform is determined in the world coordinate system as follows: in , and The mobile platform is respectively in the world coordinate system , and Velocity component in the axial direction; is the heading angular velocity of the mobile platform; is the radius of the left and right driving wheels of the mobile platform, , are the forward speeds of the left and right driving wheels respectively; Integrating the kinematic model to obtain a position of a mounting point of the robotic arm on the mobile platform in the world coordinate system and a heading angle of the mobile platform; in , and The installation point in the world coordinate system is , and Position in the axial direction; is the heading angle of the mobile platform.

3. The motion planning method for a two-wheeled mobile manipulator based on an iterative learning network according to claim 2, characterized in that: The robotic arm is a six-degree-of-freedom robotic arm, and obtaining a homogeneous transformation matrix of the robotic arm joints according to the structural parameters of the robotic arm includes: Establish a robot coordinate system with the forward direction of the mobile platform as the positive direction of the X axis, the line between the left and right drive wheels as the Y axis, and the installation point as the origin; Obtain the homogeneous transformation matrices corresponding to the six joints of the robotic arm respectively through the DH modeling method; Determining the pose vector of the end effector of the robotic arm according to the homogeneous transformation matrix includes: The pose vector of the end effector of the robotic arm is calculated based on the comprehensive homogeneous transformation matrix and the homogeneous transformation matrices corresponding to the six joints of the robotic arm as follows: in , represents the pose vector of the end effector of the robotic arm; represents the comprehensive homogeneous transformation matrix from the world coordinate system to the robot coordinate system, Respectively represent the homogeneous transformation matrices corresponding to the six joints of the robotic arm; Determining the inverse kinematics equation of the robotic arm according to the posture vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform includes: The Jacobian matrix of the end effector is obtained as follows: in The vector consisting of the angles of the six joints and the rotation angles of the left and right driving wheels is used as the first vector, and the time derivative of the first vector is defined as , represents the vector composed of the rotational angular velocities of the six joints and the rotational angular velocities of the left and right driving wheels as the second vector; The inverse kinematics equation of the robotic arm is determined as follows: in is the derivative of the pose vector.

4. The motion planning method for a two-wheeled mobile manipulator based on an iterative learning network according to claim 3, characterized in that: Determining a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation, and a repeatable target task includes: Using the repeated motion index as the optimization index, under the constraints of the inverse kinematics equation, the first motion planning model is determined as follows: in express The second norm of represents a coefficient vector; The penalty function method is used to convert the first motion planning model with equality constraints into the second motion planning model without constraints as follows: in represents the penalty factor, Denotes the Jacobian matrix No. v OK, Represents a vector No. v OK.

5. The motion planning method for a two-wheeled mobile manipulator based on an iterative learning network according to claim 4, characterized in that: The determining of the iterative learning network solution algorithm comprises: The partial derivatives of the unconstrained second motion planning model with respect to the control variables are calculated as follows: in represents the number of iterations of the target task, the time derivative As the control variable, the control variable is expressed as , is the pose vector actually measured by the end effector; The tracking errors generated when all follower manipulators track the target trajectory of the leader manipulator are calculated according to the error calculation expression, wherein the manipulators include one leader manipulator and multiple follower manipulators. The error calculation expression is as follows: The iterative learning network solution algorithm is determined as follows: in represents the learning step size, represents the input of iterative learning, The update law is determined as: in Input gain matrix for the iteration.

6. The motion planning method for a two-wheeled mobile manipulator based on an iterative learning network according to claim 5, characterized in that: The method of transmitting the angular velocity of each joint of the robotic arm and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller includes: Integrating the angular velocities of the joints of the robotic arm and the angular velocities of the left and right driving wheels of the mobile platform to obtain the optimal joint angular velocities and optimal left and right driving wheel angular velocities that best match the target task; The optimal joint angular velocity and the optimal left and right driving wheel angular velocity are transmitted to the lower computer controller.

7. A two-wheeled mobile manipulator motion planning system based on iterative learning network, characterized in that: include: The first module is used to obtain a kinematic model of the mobile platform according to the structural parameters of the two-wheel differential drive mobile platform; The second module is used to obtain the homogeneous transformation matrix of the joints of the robotic arm according to the structural parameters of the robotic arm; Determining a pose vector of an end effector of the robotic arm according to the homogeneous transformation matrix; determining an inverse kinematics equation of the robotic arm according to the pose vector, the joint angular velocity of the robotic arm, and the angular velocity of the left and right driving wheels of the mobile platform; the robotic arm is disposed on the mobile platform; A third module is used to determine a robotic arm motion planning model based on quadratic programming according to the kinematic model, the inverse kinematics equation and a repeatable target task; A fourth module is configured to determine an iterative learning network solution algorithm and use the iterative learning network solution algorithm to solve the robotic arm motion planning model to obtain the angular velocity of each joint of the robotic arm and the angular velocity of the left and right drive wheels of the mobile platform; The fifth module is used to transmit the angular velocity of each joint of the manipulator and the angular velocity of the left and right driving wheels of the mobile platform to the lower computer controller, so that the lower computer controller drives the joints of the manipulator and the mobile platform to move, and makes the end effector of the manipulator track the target trajectory in the manipulator motion planning model; The step of determining the iterative learning network solution algorithm comprises the following steps: Obtaining the Jacobian matrix of the end effector; Obtaining a vector consisting of the rotational angular velocities of the six joints of the robotic arm and the rotational angular velocities of the left and right driving wheels; Using a repeated motion index as an optimization index, and under the constraints of the inverse kinematics equation, determining a first motion planning model; Converting the first motion planning model with equality constraints into a second motion planning model without constraints by using a penalty function method; Calculating and obtaining the partial derivative of the unconstrained second motion planning model with respect to the control variable; The iterative learning network solution algorithm is determined according to the Jacobian matrix, the vector, and the partial derivative.

8. A two-wheeled mobile robotic arm, characterized in that: include: Two-wheel differential-driven mobile platform and robotic arm; The two-wheel mobile robotic arm is used to respond to the driving operation of the lower computer controller so that the end effector of the robotic arm tracks the target trajectory in the robotic arm motion planning model; The driving operation of the lower computer controller is determined according to a two-wheeled mobile manipulator motion planning method based on an iterative learning network according to any one of claims 1 to 6.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

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