论文标题
蒙特卡洛树搜索步态策划者，用于非基准腿系统控制
Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control
论文作者
论文摘要
在这项工作中，提出了用于腿部系统运动的非对准框架。该方法通过将问题视为决策过程来解除步态序列优化。重新定义的接触序列问题是通过利用蒙特卡洛树搜索（MCT）算法来解决的，该算法利用了基于优化的模拟来评估最佳搜索方向。事实证明，与最先进的混合二次二次编程（MIQP）相比，拟议的计划在探索和探索空间的开发之间取决于良好的权衡。模型预测控制（MPC）利用MCT产生的步态来优化地面反应力和未来的立足点位置。在四倍的机器人上进行的仿真结果表明，所提出的框架可以生成已知的周期步态并将接触序列调整到遇到的条件下，包括具有未知和可变特性的外部力和地形。当对具有不同布局的机器人进行测试时，系统还显示了其可靠性。
In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-making process. The redefined contact sequence problem is solved by utilizing a Monte Carlo Tree Search (MCTS) algorithm that exploits optimization-based simulations to evaluate the best search direction. The proposed scheme has proven to have a good trade-off between exploration and exploitation of the search space compared to the state-of-the-art Mixed-Integer Quadratic Programming (MIQP). The model predictive control (MPC) utilizes the gait generated by the MCTS to optimize the ground reaction forces and future footholds position. The simulation results, performed on a quadruped robot, showed that the proposed framework could generate known periodic gait and adapt the contact sequence to the encountered conditions, including external forces and terrain with unknown and variable properties. When tested on robots with different layouts, the system has also shown its reliability.
