A Novel Version of Sampling-based Motion Planner for Manipulation with Faster Initial Solution and Convergence
- Resource Type
- Conference
- Authors
- Zhao, Guoqiang; Wang, Xiangzhou; Zheng, Shuhua; Han, Qian
- Source
- 2022 34th Chinese Control and Decision Conference (CCDC) Control and Decision Conference (CCDC), 2022 34th Chinese. :357-363 Aug, 2022
- Subject
- General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Costs
Simulation
Heuristic algorithms
Optimization methods
Manipulators
Planning
Task analysis
Sampling-Based Algorithm
Manipulation motion planning
Industrial robots
- Language
- ISSN
- 1948-9447
This paper presents, Anytime Fast-BIT* (AFBIT*), a sampling based, asymptotically optimal manipulation motion planner which quickly finds an initial feasible path and rapidly improves the path quality toward optimality. AFBIT* is guided by modified heuristics in task space for faster first solution. A local optimization method is adopted at the end of every round to optimize the current best path and generate the next vertex and edge for the global path while a new round begins to improve the quality of the remaining path. Simulation results suggest AFBIT* is more efficient and effective on manipulation problems than BIT* and Fast-BIT*.