融合全局规划与实时响应，让机器人在动态环境中稳定避障
SBAMP: Sampling Based Adaptive Motion Planning
- 用RRT*生成最优路径，再通过在线控制器实时调整
- 实验显示可快速恢复扰动，硬件测试表现稳定可靠
- 无需预训练数据，适合真实机器人部署
自主机器人在动态环境中需兼顾全局路径最优性与对扰动的实时响应，这涉及计算成本高的全局规划与快速局部适应之间的根本权衡。采样类规划器如RRT*虽能生成近优路径，但在扰动下表现不佳；而动力系统方法如SEDS可实现平滑反应行为，但依赖离线数据驱动优化。本文提出采样式自适应运动规划（SBAMP），融合基于RRT*的全局规划与在线、李雅普诺夫稳定的SEDS启发式控制器，无需预训练数据。通过在控制环路中引入轻量级约束优化，SBAMP在保持全局路径结构的同时实现稳定、实时适应。仿真与RoboRacer硬件实验表明，该方法能有效应对扰动，可靠处理障碍物，并在动态条件下保持一致性能。
原文摘要 · Abstract (English)
Autonomous robots operating in dynamic environments must balance global path optimality with real-time responsiveness to disturbances. This requires addressing a fundamental trade-off between computationally expensive global planning and fast local adaptation. Sampling-based planners such as RRT* produce near-optimal paths but struggle under perturbations, while dynamical systems approaches like SEDS enable smooth reactive behavior but rely on offline data-driven optimization. We introduce Sampling-Based Adaptive Motion Planning (SBAMP), a hybrid framework that combines RRT*-based global planning with an online, Lyapunov-stable SEDS-inspired controller that requires no pre-trained data. By integrating lightweight constrained optimization into the control loop, SBAMP enables stable, real-time adaptation while preserving global path structure. Experiments in simulation and on RoboRacer hardware demonstrate robust recovery from disturbances, reliable obstacle handling, and consistent performance under dynamic conditions.
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