用并行仿真学习动态安全管，让机器人在复杂环境里又快又安全。
Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
- 通过大规模并行仿真学习动作与跟踪误差的关系，构建动态安全管。
- 在3D跳跃机器人ARCHER上实现狭窄通道无碰撞高速穿越。
- 实时平衡性能与安全，适合需要高鲁棒性的移动机器人场景。
在复杂环境中实现安全导航是机器人领域的关键挑战。传统方法将规划与跟踪分离：规划基于简化模型生成参考轨迹，控制则在全阶动力学上跟踪这些轨迹。不可避免的跟踪误差要求对规划进行鲁棒化以保障安全；许多方法采用最坏情况边界，但忽略了规划模型某些轨迹实际更易跟踪的事实。本文提出一种新方法，利用大规模并行仿真学习动态安全管，表征跟踪性能随规划动作的变化。优化规划轨迹使动态安全管始终位于自由空间内，实现在性能与安全间的实时权衡。所提出的Dynamic Tube MPC应用于3D跳跃机器人ARCHER，实现了在复杂环境中的敏捷导航及狭窄通道的安全无碰撞通行。
原文摘要 · Abstract (English)
Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed on a reduced order model to generate reference trajectories, and control techniques used to track these trajectories on the full order dynamics. Inevitable tracking error necessitates robustification of the nominal plan to ensure safety; in many cases, this is accomplished via worst-case bounding, which ignores the fact that some trajectories of the planning model may be easier to track than others. In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation, which characterizes tracking performance as a function of actions taken by the planning model. Planning model trajectories are then optimized such that the dynamic tube lies in the free space, allowing a balance between performance and safety to be traded off in real time. The resulting Dynamic Tube MPC is applied to the 3D hopping robot ARCHER, enabling agile and performant navigation of cluttered environments, and safe collision-free traversal of narrow corridors.
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