An obstacle avoidance algorithm for robot manipulators based on decision-making force. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Wei; Cheng, Hongtai; Hao, Lina; Li, Xingchen; Liu, Mingfang; Gao, Xifeng
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 71(2021)
• Publication Date: 2021-10
• Subjects: Robot manipulator -- Obstacle avoidance -- Dynamic repulsion field -- Decision-making force; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2020.102114
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human's complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations. Highlights: Decision-making force can prevent the robot from falling into the local minimum. For the same configuration, DRDF can generate multiple feasible paths. DRDF's performance is verified in a simulation and experimental platform. Compared with DMPSF and DMPDF, the DRDF shows more robustness and smoothness.