Navigation Method Based on Improved Rapid Exploration Random Tree Star-Smart (RRT?-Smart) and Deep Reinforcement Learning

• Published in: 东华大学学报（英文版） Vol. 39; no. 5; pp. 490 - 495
• Main Authors: ZHANG Jue, LI Xiangjian, LIU Xiaoyan, LI Nan, YANG Kaiqiang, ZHU Heng
• Format: Journal Article
• Language: English
• Published: College of Information Science and Technology,Donghua University,Shanghai 201620,China 31.10.2022; Engineering Research Center of Digitized Textile&Fashion Technology,Ministry of Education,Shanghai 201620,China%College of Information Science and Technology,Donghua University,Shanghai 201620,China
• Subjects: rapid exploration random tree star smart (RRT?-Smart); Gaussian fitting; deep reinforcement learning (DRL); mixed actor critic (MAC)
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202202458

TP242.6; A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart (GFS RRT?-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic (MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.