Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree

TL;DR

This paper introduces a novel coordinative planning method using Monte Carlo tree search for hexapod robots, enhancing their ability to navigate complex terrains with sparse footholds by considering gait and foothold planning as a sequence optimization problem.

Contribution

It proposes a new integrated planning approach combining gait and foothold planning with fault tolerance, utilizing FastMCTS and SlidingMCTS to improve passability in challenging environments.

Findings

Significantly improves passability in sparse foothold environments.

Outperforms traditional planning methods on complex terrains.

Enhances fault tolerance in gait planning.

Abstract

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.