Robot obstacle avoidance method, computer readable storage medium, and robot

Patent metadata

Field	Value
Publication number	US-12115673-B2
Application number	US-202218052535-A
Country	US
Kind code	B2
Filing date	Nov 3, 2022
Priority date	Nov 16, 2021
Publication date	Oct 15, 2024
Grant date	Oct 15, 2024

Abstract

A robot obstacle avoidance method, a robot controller using the same, and a storage medium are provided. The method includes: determining an influence value of an obstacle on a motion range of a joint of the robot according to a position of the obstacle in a workspace of the robot; establishing a state transition relationship of the robot by taking a joint velocity of the robot as a control target and a joint angular velocity of the robot as a control input quantity; and avoiding the robot from colliding with the obstacle during a movement process of the robot by performing a model predictive control on the robot according to the state transition relationship and the influence value. In the present disclosure, the influence of the obstacle on the motion range of the joint of the robot is fully considered.

First claim

What is claimed is:

1. A computer-implemented obstacle avoidance method for a robot, comprising:
determining an influence value of an obstacle on a motion range of a joint of the robot according to a position of the obstacle in a workspace of the robot;
establishing a state transition relationship of the robot by taking a joint velocity of the robot as a control target and a joint angular velocity of the robot as a control input quantity; and
avoiding the robot from colliding with the obstacle during a movement process of the robot by performing a model predictive control on the robot according to the state transition relationship and the influence value.

2. The method of claim 1 , wherein the performing the model predictive control on the robot according to the state transition relationship and the influence value comprises:
obtaining a current state quantity of the robot, and determining a predicted state quantity of the robot at each control time point within a predetermined duration according to the current state quantity and the state transition relationship;
obtaining an expected state quantity of the robot at each control time point within the predetermined duration, and determining a current control input quantity of the robot by performing an optimal control on the control input quantity of the robot according to the predicted state quantity, the expected state quantity and the influence value; and
controlling the robot according to the current control input quantity.

3. The method of claim 2 , wherein the determining the predicted state quantity of the robot at each control time point within the predetermined duration according to the current state quantity and the state transition relationship comprises:
determining the predicted state quantity of the robot at each control time point within the predetermined duration based on an equation of:
X ( k+i )= F ( X ( k+i− 1), u ( k+i− 1))= F ( ( X ( k ), u ( k )), u ( k+i− 1)

where, i is a serial number of each control time point within the predetermined duration, 0≤i≤N, N is the total number of the control time points within the predetermined duration, F is the state transition relationship, u(k+i) is the control input quantity of the robot at the i-th control time point within the predetermined duration, X(k) is the current state quantity, and X(k+i) is the predicted state quantity of the robot at the i-th control time point within the predetermined duration.

4. The method of claim 3 , wherein the determining the current control input quantity of the robot by performing the optimal control on the control input quantity of the robot according to the predicted state quantity, the expected state quantity and the influence value comprises:
building a control objective function of the robot based on the predicted state quantity, the expected state quantity, and the influence value;
obtaining a first control input quantity of the robot at each control time point within the predetermined duration by taking minimizing the control objective function as an optimization goal to perform a nonlinear programming solving on the control input quantity after the optimal control is performed; and
determining the first control input quantity at a first control time point within the predetermined duration as the current control input quantity of the robot.

5. The method of claim 4 , wherein the building the control objective function of the robot based on the predicted state quantity, the expected state quantity, and the influence value comprises:
building the control objective function as an equation of:

∑

i
=
0

N



y
d

(

k
+
i

)

-

X
⁡
(

k
+
i

)



2

+

∑

i
=
0

N
-
1



u
⁡
(

k
+
i

)



2

+

∑

i
=
0

N

∑

j
=
1

n

α
j

;

where, y d (k+i) is the expected state quantity of the robot at the i-th control time point within the predetermined duration, j is a joint serial number of the robot, 1≤j≤n, n is the total number of joints of the robot, and α j is the influence value of the obstacle on the motion range of the j-th joint of the robot.

6. The method of claim 1 , wherein the determining the influence value of an obstacle on the motion range of the joint of the robot according to the position of the obstacle in the workspace of the robot comprises:
obtaining a joint angle upper limit of the robot, and calculating the joint angle upper limit after correction based on the joint angle upper limit and the position of the obstacle;
obtaining a joint angle lower limit of the robot, and calculating the joint angle lower limit after correction based on the joint angle lower limit and the position of the obstacle; and
calculating the influence value of the obstacle on the motion range of the joint of the robot based on the joint angle upper limit, the joint angle lower limit, the joint angle upper limit after correction, and the joint angle lower limit after correction.

7. The method of claim 6 , wherein the calculating the influence value of the obstacle on the motion range of the joint of the robot based on the joint angle upper limit, the joint angle lower limit, the joint angle upper limit after correction, and the

Assignees

• Ubtech Robotics Corp Ltd

Inventors

• Líu Yizhang
• Xiong Youjun
• Zeng Xianwen
• Zhang Zhihao
• Zhang Meihui
• Chen Chunyu

Classifications

• B25J9/163

  learning, adaptive, model based, rule based expert control · CPC title

• B25J9/1666 Primary

  Avoiding collision or forbidden zones · CPC title

• B25J9/1676 Primary

  Avoiding collision or forbidden zones · CPC title

• B25J9/1602 Primary

  characterised by the control system, structure, architecture · CPC title