A three-wheel omnidirectional mobile robot arm self-motion planning method and device

Abstract

The present application relates to a kind of three-wheel omni-directional mobile manipulator self-motion planning method, method includes: S1, using gradient descent formula, the speed layer index of manipulator is deduced from different initial state adjustment to desired state;S2, introduce end positioning error and the feedback of its integral, and combine the kinematics equation of mobile platform and the speed layer index of S1, design self-motion planning scheme described in the form of pseudo-inverse;S3, obtain the initial state of manipulator, the initial state is substituted into self-motion planning scheme and iteratively calculated, obtain the rotational angular velocity of drive wheel and joint speed of self-motion planning;S4, the rotational angular velocity of drive wheel and joint speed of self-motion planning is sent to the lower computer controller of three-wheel omni-directional mobile manipulator, drive three-wheel omni-directional mobile manipulator and mobile platform reach desired state.Compared with prior art, the present application has the advantages of automatic, fast, accurate adjustment between different states etc..

Description

TECHNICAL FIELD

[0001] The present application relates to the field of motion planning and control of a mechanical arm, and particularly to a self-motion planning method and device for a three-wheel omnidirectional mobile mechanical arm. BACKGROUND

[0002] Due to the mobility of the platform and the operability of the mechanical arm, the three-wheel omnidirectional mobile mechanical arm has a very flexible workspace and is widely used in many fields such as military, space, industry, logistics and medical treatment. In the application research of the three-wheel omnidirectional mobile mechanical arm, self-motion planning plays a key role; that is, the platform and the mechanical arm are automatically adjusted to the respective desired states while keeping the position of the end of the mechanical arm unchanged. In the planning control process, by timely adjusting the state of the three-wheel omnidirectional mobile mechanical arm, collision with environmental obstacles can be prevented, and singular or unknown states can also be prevented to avoid damage to the mobile mechanical arm. At present, self-motion planning schemes have been proposed and applied to the three-wheel omnidirectional mobile mechanical arm, but related research still needs to be further deepened. The planning of the three-wheel omnidirectional mobile mechanical arm currently performs poorly in the presence of noise interference, and the efficiency and accuracy of adjusting different states are poor. SUMMARY

[0003] The purpose of the present application is to provide a self-motion planning method for a three-wheel omnidirectional mobile mechanical arm to improve the efficiency and accuracy of state adjustment of the three-wheel omnidirectional mobile mechanical arm under noise interference.

[0004] The purpose of the present application can be achieved by the following technical solutions:

[0005] A self-motion planning method for a three-wheel omnidirectional mobile mechanical arm, the method comprising:

[0006] S1, using a gradient descent formula to derive a velocity layer index of the mechanical arm adjusting from different initial states to a desired state, the mechanical arm being installed on a mobile platform;

[0007] S2, introducing feedback of end positioning error and integral thereof, and combining the kinematics equation of the mobile platform and the velocity layer index of S1 to design a self-motion planning scheme described in a pseudo-inverse form;

[0008] S3, obtaining an initial state of the mechanical arm, substituting the initial state into the self-motion planning scheme for iterative calculation to obtain a drive wheel rotation angular velocity and a joint speed

[0009] S4, substituting the drive wheel rotation angular velocity and the joint speed The lower computer controller is sent to the three-wheel omnidirectional mobile manipulator to drive the three-wheel omnidirectional mobile manipulator and the mobile platform to reach the desired state.

[0010] Further, the velocity layer index is:

[0011]

[0012] wherein p x ∈R, p y ∈R and φ∈R represent the position of the mobile platform on the X-axis and Y-axis, i.e. the position of the manipulator mounted on the mobile platform, and the orientation angle of the mobile platform, and respectively represent the time derivative of p x , p y and φ, p xd ∈R, p yd ∈R and φ d ∈R represent the desired position of the mobile platform on the X-axis and Y-axis and the desired orientation angle; θ∈R n and respectively represent the joint angle and joint velocity of the manipulator, θ d ∈R n represents the desired joint angle of the manipulator.

[0013] Further, the self-motion planning scheme described in the pseudo-inverse form is:

[0014]

[0015] wherein Δ is the velocity layer index, represents the joint velocity vector of the mobile manipulator, J∈R m×(3+n) represents the Jacobian matrix of the mobile manipulator, P∈R (3+n)×m represents the pseudo-inverse matrix of J, α>0∈R and β>0∈R represent the error feedback coefficients, e(t)∈R m represents the end positioning error of the mobile manipulator, δ∈R represents the integral variable; λ>0∈R represents the self-motion coefficient, I 3+n ∈R (3+n)×(3+n) represents the unit matrix, M=[W - ,0;0,I n ]∈R (3+n)×(3+n) represents the augmented coefficient matrix, I n ∈R n×n represents the unit matrix, n and m respectively represent the degree of freedom of the manipulator and the spatial dimension where the end of the manipulator is located.

[0016] Further, W - ∈R 3×3 represents the matrix W∈R composed of the structural parameters of the mobile platform3×3 The inverse matrix, matrix W is:

[0017]

[0018] Where μ>0∈R represents the radius of the omnidirectional drive wheel of the mobile platform, and l>0∈R represents the distance from the center point of the mobile platform to the omnidirectional drive wheel.

[0019] Furthermore, the joint velocity vector of the mobile robotic arm is:

[0020]

[0021] in, The angular velocity of the drive wheel. This refers to the joint velocity.

[0022] Furthermore, the error feedback coefficient satisfies: α 2 >β.

[0023] Furthermore, the end-effector positioning error of the mobile robotic arm is:

[0024]

[0025] in, R 3+n →R m Represents a nonlinear mapping function. The joint position vector of the moving robotic arm is defined as follows: p e ∈R m This represents the position of the end effector of the mobile robotic arm in m-dimensional space, which does not change over time.

[0026] Furthermore, the initial state specifically refers to the initial position of the robotic arm when it is mounted on the mobile platform and the orientation angle of the mobile platform.

[0027] Furthermore, based on the obtained self-motion planning, the rotational angular velocity of the drive wheel... and joint velocity By controlling the three omnidirectional wheels and each joint, the three-wheeled omnidirectional robotic arm and mobile platform are driven to reach the desired state.

[0028]

[0029] In another aspect, the present invention also proposes a self-motion planning device for a three-wheeled omnidirectional mobile robotic arm, comprising a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the above-described method.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present application introduces the feedback of end positioning error and its integral, combines the kinematic equation of the mobile platform and the velocity layer index, proposes a velocity layer index of gradient descent and an integral enhanced self-motion planning scheme, so that the three-wheel omnidirectional mobile manipulator can still complete automatic, rapid and accurate adjustment between different states even in the presence of noise interference. This has important significance and value for the research of obstacle avoidance, singular state avoidance and repeated motion planning of the mobile manipulator in complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0033] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical scheme of the present application, and gives a detailed implementation manner and specific operation process, but the protection scope of the present application is not limited to the following embodiments.

[0034] The present application proposes a method for self-motion planning of a three-wheel omnidirectional mobile manipulator, which is simple in structure, easy to implement, less in workload, and can realize self-motion planning of the three-wheel omnidirectional mobile manipulator under noise interference. The flowchart of the method is shown in Figure 1 The present application first uses the gradient descent formula to derive the velocity layer index that can realize the adjustment of the mobile manipulator from different initial states to the desired state; then introduces the feedback of the end positioning error and its integral, combines the kinematic equation of the mobile platform and the velocity layer index, and designs a self-motion planning scheme described in the pseudo-inverse form; finally, an initial value is given, and the rotational angular velocity of the mobile platform and the joint speed of the manipulator are calculated according to the evolution of the self-motion planning scheme, and the lower computer controller of the three-wheel omnidirectional mobile manipulator drives the three omnidirectional wheels and the joints to make the mobile platform and the manipulator reach the desired state at the same time.

[0035] The self-motion planning method of the three-wheel omnidirectional mobile manipulator proposed by the present application includes the following steps:

[0036] S1, using the gradient descent formula to derive the velocity layer index of the manipulator adjusting from different initial states to the desired state, the manipulator being installed on a mobile platform;

[0037] S2, introducing the feedback of the end positioning error and its integral, combining the kinematic equation of the mobile platform and the velocity layer index of S1, and designing a self-motion planning scheme described in the pseudo-inverse form;

[0038] S3, obtaining the initial state of the manipulator, substituting the initial state into the self-motion planning scheme for iterative calculation to obtain the rotational angular velocity and the joint speed

[0039] S4, the rotation angular velocity of the driving wheel from the self-motion planning and joint velocity is sent to the lower computer controller of the three-wheel omnidirectional mobile manipulator, driving the three-wheel omnidirectional mobile manipulator and the mobile platform to reach the desired state.

[0040] In S1, the gradient descent formula is used to derive the velocity layer index that can realize the adjustment of the mobile manipulator from different initial states to the desired state; the derived velocity layer index expression is as follows:

[0041]

[0042] wherein p x ∈R, p y ∈R and φ∈R represent the position of the mobile platform on the X-axis and Y-axis (i.e. the position of the manipulator mounted on the mobile platform) and the orientation angle of the mobile platform, and represent the time derivatives of p x , p y and φ, p xd ∈R, p yd ∈R and φ d ∈R represent the desired position of the mobile platform on the X-axis and Y-axis and the desired orientation angle; θ∈R n and represent the joint angle and joint velocity of the manipulator, θ d ∈R n represents the desired joint angle of the manipulator.

[0043] In S2, the feedback of the end positioning error and its integral is introduced, combined with the kinematics equation of the mobile platform and the velocity layer index, to design a self-motion planning scheme described in the pseudo-inverse form; for the three-wheel omnidirectional mobile manipulator, the designed self-motion planning scheme is described as follows:

[0044]

[0045] wherein, represents the joint velocity vector of the mobile manipulator, J∈R m×(3+n) represents the Jacobian matrix of the mobile manipulator, P∈R (3+n)×m represents the pseudo-inverse matrix of J; α>0∈R and β>0∈R represent the error feedback coefficients; e(t)∈R m represents the end positioning error of the mobile manipulator, t>0∈R represents the time variable; δ∈R represents the integral variable; λ>0∈R represents the self-motion coefficient, I 3+n ∈R (3+n)×(3+n) represents the unit matrix; M∈R (3+n)×(3+n)denotes the augmented coefficient matrix, n and m denote the number of degrees of freedom of the robot arm and the dimension of the space where the robot arm is located, respectively.

[0046] In S3, given an initial value, the rotation angular velocity of the mobile platform and the joint velocity of the robot arm

[0047] In S4, the lower computer controller of the three-wheel omnidirectional mobile robot arm drives the three omnidirectional wheels and each joint according to these speed data to make the mobile platform and the robot arm reach the desired state at the same time so as to achieve the purpose of self-motion planning.

[0048] The three-wheel omnidirectional mobile robot arm self-motion planning method based on gradient descent and integral enhancement design has the following advantages compared with the prior art:

[0049] The present application can effectively overcome the shortcomings of the prior art, and provides a self-motion planning method described in the pseudo-inverse form, which can enable the three-wheel omnidirectional mobile robot arm to complete automatic, rapid and accurate adjustment between different states even in the presence of noise interference; this has important significance and value for the research of obstacle avoidance, singular state avoidance and repeated motion planning of the mobile robot arm in complex environments.

[0050] The present application also provides a three-wheel omnidirectional mobile robot arm self-motion planning device, which comprises a memory, a processor, and a program stored in the memory, and the processor implements the above method when executing the program.

[0051] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment on the basis of the prior art according to the concept of the present application shall be within the protection scope defined by the claims.

Claims

1. A self-motion planning method for a three-wheeled omnidirectional robotic arm, characterized in that the method... include: S1. Using the gradient descent formula, derive the speed layer index of the robotic arm adjusting from different initial states to the desired state, wherein the robotic arm is mounted on a mobile platform; S2. Introduce feedback of end-positioning error and its integral, and combine the kinematic equations of the mobile platform and the velocity layer index of S1 to design a self-motion planning scheme described in pseudo-inverse form. S3. Obtain the initial state of the robotic arm, substitute the initial state into the self-motion planning scheme for iterative calculation, and obtain the rotational angular velocity of the drive wheel in the self-motion planning. and joint velocity S4, the rotational angular velocity of the drive wheel according to the self-motion planning. and joint velocity The data is sent to the lower-level controller of the three-wheeled omnidirectional robotic arm, driving the three-wheeled omnidirectional robotic arm and the mobile platform to reach the desired state; the speed level indicator is: Where, p x ∈R, p y ∈R and φ∈R represent the positions of the mobile platform on the X and Y axes, respectively, which are the positions where the robotic arm is mounted on the mobile platform and the orientation angle of the mobile platform. and They represent p respectively x p y The time derivative of φ, p xd ∈R, p yd ∈R and φ d θ ∈ R represents the desired position and desired orientation angle of the mobile platform on the X-axis and Y-axis, respectively; θ ∈ R n and θ represents the joint angle and joint velocity of the robotic arm, respectively. d ∈R n The desired joint angles of the robotic arm are represented; the self-motion planning scheme described in pseudo-inverse form is: Where Δ is the velocity layer index, Let J ∈ R be the joint velocity vector of the mobile robotic arm. m×(3+n) The Jacobian matrix representing the mobile robotic arm, P∈R (3+n)×m Let J be the pseudo-inverse matrix, α > 0 ∈ R and β > 0 ∈ R, and let e(t) ∈ R. m The positional error of the mobile robotic arm is represented by δ∈R, where δ∈R represents the integral variable; λ>0∈R represents the self-motion coefficient, and I 3+n ∈R (3+n)×(3+n) Denotes the identity matrix, M = [W - ,0;0,I n ]∈R (3+n)×(3+n) Let I represent the augmented coefficient matrix. n ∈R n×n W represents the identity matrix, where n and m represent the number of degrees of freedom of the robotic arm and the spatial dimension of the robotic arm's end effector, respectively. - ∈R 3×3 The matrix W∈R represents the structure parameters of the mobile platform. 3×3 The inverse matrix.

2. The self-motion planning method for a three-wheeled omnidirectional robotic arm according to claim 1, characterized in that, Matrix W is: Where μ>0∈R represents the radius of the omnidirectional drive wheel of the mobile platform, and l>0∈R represents the distance from the center point of the mobile platform to the omnidirectional drive wheel.

3. The self-motion planning method for a three-wheeled omnidirectional mobile robotic arm according to claim 1, characterized in that, The joint velocity vector of the mobile robotic arm is: in, The angular velocity of the drive wheel. This refers to the joint velocity.

4. The self-motion planning method for a three-wheeled omnidirectional robotic arm according to claim 1, characterized in that, The error feedback coefficients satisfy: α 2 >β.

5. The self-motion planning method for a three-wheeled omnidirectional robotic arm according to claim 1, characterized in that, The end-effector positioning error is: in, Represents a nonlinear mapping function, θ∈R 3+n The joint position vector of the moving robotic arm is defined as θ = [p x ;p y ;φ;θ],p e ∈R m This represents the position of the end effector of the mobile robotic arm in m-dimensional space, which does not change over time.

6. The self-motion planning method for a three-wheeled omnidirectional robotic arm according to claim 1, characterized in that, The initial state specifically refers to the initial position of the robotic arm when it is mounted on the mobile platform and the orientation angle of the mobile platform.

7. The self-motion planning method for a three-wheeled omnidirectional robotic arm according to claim 1, characterized in that, Based on the obtained self-motion programming, the rotational angular velocity of the drive wheel and joint velocity By controlling the three omnidirectional wheels and each joint, the three-wheeled omnidirectional robotic arm and mobile platform are driven to reach the desired state.

8. A self-motion planning device for a three-wheeled omnidirectional robotic arm, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-7.

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