让机器人路径跟踪更安全，能自动避障并恢复而不需重规划。
Trajectory Tracking with Reachability-Guided Quadratic Programming and Freeze-Resume
- 基于可达性分析的二次规划方法，确保运动受速度与加速度约束。
- 在线处理干扰时可拒绝最大扰动，状态对齐时实现精准跟踪。
- 适合需要安全暂停与快速恢复的工业机器人系统。
许多机器人系统需遵循预设路径，同时在人或物体干预时安全暂停并恢复。本文提出一种输出空间方法，适用于其输出可通过反馈线性化为双积分器的系统（如机械臂）。离线阶段，执行预运行可达性检查，验证轨迹满足速度与加速度幅值限制；在线阶段，采用二次规划在相同限制下跟踪轨迹。通过一步可达性测试，界定系统能拒绝的最大扰动。当系统状态与参考路径一致时，在确定性情况下可实现完美跟踪，并利用基于KKT的权重修正误差。实验表明，该系统能高效处理安全停机和非计划偏离，无需重规划即可返回原轨迹。仿真结果显示，其性能优于纯追踪方法。
原文摘要 · Abstract (English)
Many robotic systems must follow planned paths yet pause safely and resume when people or objects intervene. We present an output-space method for systems whose tracked output can be feedback-linearized to a double integrator (e.g., manipulators). The approach has two parts. Offline, we perform a pre-run reachability check to verify that the motion plan respects speed and acceleration magnitude limits. Online, we apply a quadratic program to track the motion plan under the same limits. We use a one-step reachability test to bound the maximum disturbance the system is capable of rejecting. When the state coincides with the reference path we recover perfect tracking in the deterministic case, and we correct errors using a KKT-inspired weight. We demonstrate that safety stops and unplanned deviations are handled efficiently, and the system returns to the motion plan without replanning. We demonstrate our system's improved performance over pure pursuit in simulation.
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