Title:
MULTI-ROBOT TRAJECTORY PLANNING METHOD
Document Type and Number:
WIPO Patent Application WO/2022/241808
Kind Code:
A1
Abstract:
Disclosed in the present invention is a multi-robot trajectory planning method. The method comprises the following steps: obtaining a current trajectory vector by means of analysis during deep Q-learning and by using a state of a multi-robot surrounding environment, designing a reward network of deep Q-learning, and taking both the current trajectory vector and a desired trajectory vector as inputs of the reward network, and an output of the reward network as reward information, and training parameters of a convolutional neural network (CNN) by using the inputs and the reward information; taking the current trajectory vector as an input of the CNN, and the CNN, which has been trained on the basis of the reward information, outputting corresponding action information to environment information by using a CNN algorithm; and then rationally allocating all actions related to a workpiece to multiple robots by using a resource-based multi-robot task allocation algorithm, such that the multiple robots can cooperate with each other without interfering with each other, thereby implementing spatial three-dimensional complex trajectory planning for multiple robots, and thus achieving the high efficiency of the robots cooperatively executing a complex task.
Inventors:
ZHANG GONG (CN)
HOU ZHICHENG (CN)
YANG WENLIN (CN)
LV HAOLIANG (CN)
WU YUEYU (CN)
XU ZHENG (CN)
LIANG JIMIN (CN)
ZHANG ZHIBIAO (CN)
HOU ZHICHENG (CN)
YANG WENLIN (CN)
LV HAOLIANG (CN)
WU YUEYU (CN)
XU ZHENG (CN)
LIANG JIMIN (CN)
ZHANG ZHIBIAO (CN)
Application Number:
PCT/CN2021/095970
Publication Date:
November 24, 2022
Filing Date:
May 26, 2021
Export Citation:
Assignee:
GUANGZHOU INSTITUTE OF ADVANCED TECH CHINESE ACADEMY OF SCIENCES (CN)
International Classes:
G06K9/62; G06N3/04; G06N3/08
Foreign References:
| CN112596515A | 2021-04-02 | |||
| CN109906132A | 2019-06-18 | |||
| CN109540150A | 2019-03-29 | |||
| CN109839933A | 2019-06-04 | |||
| US10733535B1 | 2020-08-04 | |||
| JP2020082314A | 2020-06-04 | |||
| CN110083166A | 2019-08-02 |
Other References:
SUI BOWEN, HUANG ZHIJIAN, JIANG BAOXIANG, ZHENG HUAN, WEN JIAYI: "Path planning algorithm for unmanned surface vessels based on deep Q network", JOURNAL OF SHANGHAI MARITIME UNIVERSITY, SHANGHAI, vol. 41, no. 3, 30 September 2020 (2020-09-30), Shanghai, XP093005735, ISSN: 1672-9498, DOI: 10.13340/j.jsmu.2020.03.001
Attorney, Agent or Firm:
GUANGZHOU RONDA INTELLECTUAL PROPERTY AGENCY (CN)
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