六足机器人双向并行蒙特卡洛树搜索步态规划

Bidirectional parallel Monte Carlo tree search gait planning for hexapod robot

中文关键词  六足机器人  步态规划  强化学习  蒙特卡洛树搜索

英文关键词  hexapod robot  gait planning  reinforcement learning  Monte Carlo tree search

中文摘要

      为了解决稀疏立足点地形中六足机器人步态规划问题, 本文提高规划时间效率、通过能力、抵达精度和运动速度, 提出了一种双向并行蒙特卡洛树搜索算法(BPMCTS). 将步态规划问题转化成马尔科夫序列优化过程, 构建相向并行拓展蒙特卡洛树结构, 搜索最佳立足位置形成步态序列; 在模拟阶段搜索过程采用深度根并行化模拟方式, 提高算法收敛速度; 在奖励评估机制引入相遇评估指标, 增强算法拓展导向性. 仿真对比实验结果表明, 所提算法规划时间效率提高46.9%, 机器人通过能力提高7.7%, 抵达精度提高32.6%, 运动速度提高16.8%, 验证了所提算法的可行性和优势性.

英文摘要

      To solve the gait planning problem of the hexapod robot in sparse foothold terrain, and improve the planning time efficiency, passing ability, arrival accuracy and motion speed, a bidirectional parallel Monte Carlo tree search algorithm (BPMCTS) is proposed. The gait planning problem is transformed into a Markov sequence optimization process. A bidirectional parallel extended Monte Carlo tree structure is constructed to search for the best base position and form gait sequences. In the simulation phase, the deep-root parallelization simulation method is adopted to improve the convergence speed of the algorithm. The encounter evaluation index is introduced in the reward evaluation mechanism to enhance the orientation of the algorithm. The results of simulation experiments show that the planning time efficiency increases by 46.9% of the proposed algorithm, the passing ability increases by 7.7%, the arrival accuracy increases by 32.6% and the motion speed increases by 16.8% of the robot, which verifies the feasibility and superiority of the proposed algorithm.