期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2018
卷号：15
期号：4
DOI：10.1177/1729881418787049
语种：English
出版社：SAGE Publications
摘要：Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing–Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing–Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing–Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.
关键词：Rapidly-exploring Random Tree; manipulation planning; local minimum; redundant manipulator; MoveIt!
Loading...
