摘要
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.
作者
张珏
李祥健
刘肖燕
李楠
杨开强
朱恒
ZHANG Jue;LI Xiangjian;LIU Xiaoyan;LI Nan;YANG Kaiqiang;ZHU Heng(College of Information Science and Technology,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitized Textile&Fashion Technology,Ministry of Education,Shanghai 201620,China)
基金
National Natural Science Foundation of China (No.61903078)。