${\ t KRAFT}$：基于采样的车辆系统近似辨识模型下的动力学重规划与反馈控制
${\tt KRAFT}$: Sampling-Based Kinodynamic Replanning and Feedback Control over Approximate, Identified Models of Vehicular Systems
AI总结：
针对近似动力学模型下复杂动力学机器人轨迹执行安全性不足的问题，提出KRAFT方法，整合采样式动力学重规划、反馈控制与安全机制，经SysId调优模型，实验验证了规划层反馈闭环与长时域推理的重要性。
摘要：
本文旨在提升具有复杂动力学特性的机器人在轻量化近似动力学模型下执行规划轨迹的安全性与可靠性。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。本文提出的方法——基于近似模型的反馈跟踪动力学重规划（Kinodynamic Replanning over Approximate Models with Feedback Tracking, KRAFT），整合了三部分：（i）通过渐近最优的基于采样的动力学树规划器进行重规划；（ii）通过反馈控制实现轨迹跟踪；（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微分方程解析表达的粗略动力学模型，该模型在训练环境中通过系统辨识（SysId）调优，而非在部署环境中调优。这使得该过程速度快，且能在每个重规划周期内实现长时域推理。同时，即便经过SysId，该模型在新环境中仍与现实存在差距。实验证明了运动学路径规划与路径跟踪方法的局限性，凸显了两点的重要性：（a）在规划层面也闭合反馈回路；（b）长时域推理，对于在不准确模型下安全高效地执行轨迹至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
