Path planning and collision avoidance based on the RRTFN framework for a robotic manipulator in various scenarios

• Published in: Complex & intelligent systems Vol. 9; no. 6; pp. 7475 - 7494
• Main Authors: Qi, Jianyou, Yuan, Qingni, Wang, Chen, Du, Xiaoying, Du, Feilong, Ren, Ao
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer
• Subjects: Boundary extension; Collision avoidance; Complexity; Computational Intelligence; Data Structures and Information Theory; Engineering; Manipulator; Original Article; Path planning; Rapidly exploring random tree-fixed node (RRTFN); Various scenarios; Rapidly exploring random tree-fixed node (RRTFN); Various scenarios; Path planning; Boundary extension; Collision avoidance; Manipulator
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-023-01131-2

In this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.