Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree
This work addresses navigation challenges for hexapod robots in complex terrains, representing an incremental advancement in legged robot planning.
The paper tackled the problem of hexapod robot navigation in sparse foothold environments by proposing a coordinative planning method that treats gait and foothold selection as a sequence optimization problem, using Monte Carlo tree search algorithms, and showed dramatic improvements in passability.
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.