用于安全独轮车机器人导航的反馈运动预测
Feedback Motion Prediction for Safe Unicycle Robot Navigation
AI总结：
本文提出一种新颖的锥形反馈运动预测方法，用于界定运动学独轮车机器人在标准控制下的闭环轨迹，结合 reference governors 实现安全绕障，数值仿真表明快速准确的反馈运动预测是快速反应式安全导航的关键。
摘要：
作为一种简单且鲁棒的移动机器人底盘，可建模为运动学独轮车的差速驱动机器人在工业和家庭场景的物流与服务机器人中有着广泛应用。在复杂杂乱的环境中，尤其是在人群和其他机器人周围，安全绕障导航是此类独轮车机器人执行各类有用任务的一项必备技能。快速准确的安全性评估在反应式安全机器人运动设计中起着关键作用。本文提出了一种新颖的锥形反馈运动预测方法，用于界定在标准独轮车运动控制方法下运动学独轮车机器人模型的闭环运动轨迹，作为标准圆形 Lyapunov 水平集更准确且依然简单的替代方案。我们展示了利用 reference governors 进行安全绕障导航的独轮车反馈运动预测应用，其中基于预测的未来机器人运动持续监测独轮车机器人的安全性。我们在数值仿真中研究了运动预测对机器人行为的作用，得出结论：快速准确的反馈运动预测是实现快速、反应式且安全的机器人绕障导航的关键。
英文摘要：
As a simple and robust mobile robot base, differential drive robots that can be modelled as a kinematic unicycle find significant applications in logistics and service robotics in both industrial and domestic settings. Safe robot navigation around obstacles is an essential skill for such unicycle robots to perform diverse useful tasks in complex cluttered environments, especially around people and other robots. Fast and accurate safety assessment plays a key role in reactive and safe robot motion design. In this paper, as a more accurate and still simple alternative to the standard circular Lyapunov level sets, we introduce novel conic feedback motion prediction methods for bounding the close-loop motion trajectory of the kinematic unicycle robot model under a standard unicycle motion control approach. We present an application of unicycle feedback motion prediction for safe robot navigation around obstacles using reference governors, where the safety of a unicycle robot is continuously monitored based on the predicted future robot motion. We investigate the role of motion prediction on robot behaviour in numerical simulations and conclude that fast and accurate feedback motion prediction is key for fast, reactive, and safe robot navigation around obstacles.
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。
