The study of combined task and motion planning has mostly been concerned with feasibility planning for high-dimensional, complex manipulation problems. Instead this paper gives its attention to optimal planning for low-dimensional planning problems and introduces the dynamic, anytime task and path planner for mobile robots. The proposed approach adopts a multi-tree extension of the T-RRT* algorithm in the path planning layer and further introduces dynamic and anytime planning components to enable low-level path correction and high-level re-planning capabilities when operating in dynamic or partially-known environments. Evaluation of the planner against existing methods show cost reductions of solution plans while remaining computationally efficient, and simulated deployment of the planner validates the effectiveness of the dynamic, anytime behavior of the proposed approach.
|Number of pages||4|
|Publication status||Accepted/In press - 16 Jan 2019|
|Event||The UKRAS19 Conference on Embedded Intelligence - Loughborough University, Loughborough, United Kingdom|
Duration: 24 Jan 2019 → 24 Jan 2019
|Conference||The UKRAS19 Conference on Embedded Intelligence|
|Period||24/01/19 → 24/01/19|
- autonomous systems
- task planning
- path planning
- combined task and motion planning
- dynamic planning
Wong, C., Yang, E., Yan, X-T., & Gu, D. (Accepted/In press). Dynamic anytime task and path planning for mobile robots. Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom.