Path Planning Algorithm for Dual-Arm Robot Based on Depth Deterministic Gradient Strategy Algorithm

• Published in: Mathematics (Basel) Vol. 11; no. 20; p. 4392
• Main Authors: Zhang, Xiaomei, Yang, Fan, Jin, Qiwen, Lou, Ping, Hu, Jiwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Algorithms; Collaboration; Collision avoidance; Data mining; Decision making; Decision theory; Deep learning; dual-arm robot; Efficiency; Factories; Machine learning; multi-agent reinforcement learning; Multiagent systems; Optimization; path planning; Robot arms; Robot control; Robot dynamics; Robotics; Robotics industry; Robots; Shortest-path problems; Simulation methods; Sparsity; Strategy
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11204392

In recent years, the utilization of dual-arm robots has gained substantial prominence across various industries owing to their collaborative operational capabilities. In order to achieve collision avoidance and facilitate cooperative task completion, efficient path planning plays a pivotal role. The high dimensionality associated with collaborative task execution in dual-arm robots renders existing path planning methods ineffective for conducting efficient exploration. This paper introduces a multi-agent path planning reinforcement learning algorithm that integrates an experience replay strategy, a shortest-path constraint, and the policy gradient method. To foster collaboration and avoid competition between the robot arms, the proposed approach incorporates a mechanism known as “reward cooperation, punishment competition” during the training process. Our algorithm demonstrates strong performance in the control of dual-arm robots and exhibits the potential to mitigate the challenge of reward sparsity encountered during the training process. The effectiveness of the proposed algorithm is validated through simulations and experiments, comparing the results with existing methods and showcasing its superiority in dual-arm robot path planning.