用近似动力学模型实现机器人安全轨迹规划与跟踪
${\tt KRAFT}$: Sampling-Based Kinodynamic Replanning and Feedback Control over Approximate, Identified Models of Vehicular Systems
- 基于采样树的动态规划，结合反馈控制与安全机制
- 在不精确模型下仍能实现长时程安全轨迹执行
- 适合动态复杂、环境未知的移动机器人应用
本文旨在提升使用轻量级近似动力学模型的非平凡动态机器人在执行轨迹时的安全性与可靠性。应用场景包括移动机器人在表面建模不完善、摩擦未知的工作空间中导航。提出的KRAFT方法整合了：(i) 基于渐近最优采样式动力学树的重规划，(ii) 基于反馈控制的轨迹跟踪，以及 (iii) 减少二阶动力学导致碰撞的安全机制。规划与控制组件使用通过系统辨识（SysId）在训练环境中调优的解析形式近似动力学模型，但不在部署环境中更新。该设计使每次重规划周期内可快速完成长时程推理。尽管经过系统辨识，模型在新环境中仍存在与真实系统的偏差。实验表明，纯运动学路径规划与路径跟踪方法存在局限，凸显了：(a) 在规划层面也需闭环反馈的重要性；(b) 长时程推理对在不准确模型下安全高效执行轨迹的关键作用。
原文摘要 · Abstract (English)
This paper aims to increase the safety and reliability of executing trajectories planned for robots with non-trivial dynamics given a light-weight, approximate dynamics model. Scenarios include mobile robots navigating through workspaces with imperfectly modeled surfaces and unknown friction. The proposed approach, Kinodynamic Replanning over Approximate Models with Feedback Tracking (KRAFT), integrates: (i) replanning via an asymptotically optimal sampling-based kinodynamic tree planner, with (ii) trajectory following via feedback control, and (iii) a safety mechanism to reduce collision due to second-order dynamics. The planning and control components use a rough dynamics model expressed analytically via differential equations, which is tuned via system identification (SysId) in a training environment but not the deployed one. This allows the process to be fast and achieve long-horizon reasoning during each replanning cycle. At the same time, the model still includes gaps with reality, even after SysID, in new environments. Experiments demonstrate the limitations of kinematic path planning and path tracking approaches, highlighting the importance of: (a) closing the feedback-loop also at the planning level; and (b) long-horizon reasoning, for safe and efficient trajectory execution given inaccurate models.
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