用蒙特卡洛树搜索与全身轨迹优化，同步规划足式机器人接触序列和着陆点。
Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
- 结合MCTS与全身轨迹优化，同步求解接触顺序与着陆区域。
- 在仿真中快速生成多种动态一致的复杂步态方案。
- 首次实现四足机器人在非循环多接触场景下的实时规划，适合复杂地形任务。
足式机器人具备在高度受限环境中进行敏捷运动的潜力。然而，规划此类运动需解决包含连续与离散变量混合的高难度优化问题。本文提出一种基于蒙特卡洛树搜索（MCTS）与全身轨迹优化（TO）的完整框架，用于在极端挑战性环境中同时规划接触序列与着陆区域。通过大量仿真实验，我们验证该框架可快速生成多样且动态一致的运动方案。实验进一步证明这些方案可成功迁移至真实四足机器人。此外，同一框架还能生成复杂的非循环人形动作。据我们所知，这是首个利用四足机器人全身动力学实现非循环多接触步态的同步接触序列与着陆区规划的成功案例。
原文摘要 · Abstract (English)
Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulation experiments, we show that our framework can quickly find a diverse set of dynamically consistent plans. We experimentally show that these plans are transferable to a real quadruped robot. We further show that the same framework can find highly complex acyclic humanoid maneuvers. To the best of our knowledge, this is the first demonstration of simultaneous contact sequence and patch selection for acyclic multi-contact locomotion using the whole-body dynamics of a quadruped.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。