モンテカルロ木探索を用いた六脚ロボットの耐故障性フリーゲイトと足場計画
Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree
六脚ロボットの歩容と足場の計画を系列最適化問題として統合し、モンテカルロ木探索で最適化することで、足場が疎な環境での通過性を大幅に向上させた研究。
著者: Liang Ding, Peng Xu, Haibo Gao, Zhikai Wang, Ruyi Zhou, Zhaopei Gong, Guangjun Liu
分類: cs.RO
原文アブストラクト
Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Traditional methods plan gait and foothold separately and treat them as the single-step optimal process. However, such processing causes its poor passability in a sparse foothold environment. This paper novelly proposes a coordinative planning method for hexapod robots that regards the planning of gait and foothold as a sequence optimization problem with the consideration of dealing with the harshness of the environment as leg fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve some defeats of the standard MCTS applicating in the field of legged robot planning. The proposed planning algorithm combines the fault-tolerant gait method to improve the passability of the algorithm. Finally, compared with other planning methods, experiments on terrains with different densities of footholds and artificially-designed challenging terrain are carried out to verify our methods. All results show that the proposed method dramatically improves the hexapod robot's ability to pass through sparse footholds environment.
