ガウス過程推論による複数ホモトピー類のオンライン動作計画
Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference
動的環境で複数の経路候補（ホモトピー類）を同時に保持し、毎ステップで到達不能部分を刈り込んで再最適化することで、ロボットが経路を動的に切り替えられるオンライン計画手法を提案した。
著者: Keshav Kolur, Sahit Chintalapudi, Byron Boots, Mustafa Mukadam
分類: cs.RO
原文アブストラクト
Efficient planning in dynamic and uncertain environments is a fundamental challenge in robotics. In the context of trajectory optimization, the feasibility of paths can change as the environment evolves. Therefore, it can be beneficial to reason about multiple possible paths simultaneously. We build on prior work that considers graph-based trajectories to find solutions in multiple homotopy classes concurrently. Specifically, we extend this previous work to an online setting where the unreachable (in time) part of the graph is pruned and the remaining graph is reoptimized at every time step. As the robot moves within the graph on the path that is most promising, the pruning and reoptimization allows us to retain candidate paths that may become more viable in the future as the environment changes, essentially enabling the robot to dynamically switch between numerous homotopy classes. We compare our approach against prior work without the homotopy switching capability and show improved performance across several metrics in simulation with a 2D robot in multiple dynamic environments under noisy measurements and execution.
