Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted. The goal of this paper is to efficiently identify obstacles in a map and remove them from the sampling space. To this end, we propose a pre-processing algorithm for space exploration that enables more efficient sampling. We show that it can boost the performance of other space sampling methods and path planners. Our approach is based on the fact that a convex obstacle can be approximated provably well by its minimum volume enclosing ellipsoid (MVEE), and a non-convex obstacle may be partitioned into convex shapes. Our main contribution is an al-gorithm that strategically finds a small sample, called the active-coreset, that adaptively samples the space via membership-oracle such that the MVEE of the coreset approximates the MVEE of the obstacle. Experimental results confirm the ef-fectiveness of our approach across multiple planners based on rapidly-exploring random trees, showing significant improve-ment in terms of time and path length.
|Title of host publication
|IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2022
|2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan
Duration: 23 Oct 2022 → 27 Oct 2022
|IEEE International Conference on Intelligent Robots and Systems
|2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
|23/10/22 → 27/10/22
Bibliographical notePublisher Copyright:
© 2022 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications