Recent Advances in Robot Trajectory Planning in a Dynamic Environment

作     者：Zhang, Hongxin Shu, Rongzijun Li, Guangsen 

作者机构：School of Mechanical Power and Engineering Harbin University of Science and Technology Harbin China Robotics & ITS Engineering Research Center Harbin University of Science and Technology Harbin150080 China 

出 版 物：《Recent Advances in Computer Science and Communications》 (Recent Advances in Computer Science and Communications)

年 卷 期：2022年第15卷第9期

页      面：1168-1183页

核心收录：

主　　题：Trajectories 

摘      要：Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment. © 2022 Bentham Science Publishers.