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.

翻译：作为一种简单且鲁棒的移动机器人平台，可建模为运动学独轮车的差速驱动机器人在工业与家庭环境中的物流与服务机器人领域具有重要应用。在复杂杂乱环境中（尤其当存在人类与其他机器人时），安全避障导航是此类独轮车机器人执行多样化有用任务的核心能力。快速而精确的安全性评估在反应式安全机器人运动设计中扮演关键角色。本文针对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边界问题，提出了一种新型锥形反馈运动预测方法，作为标准圆形李雅普诺夫水平集更精确且仍保持简洁的替代方案。我们展示了基于参考调控器的独轮车反馈运动预测在安全避障导航中的应用——通过预测的未来机器人运动持续监测其安全性。在数值仿真中探究了运动预测对机器人行为的影响，并得出结论：快速精确的反馈运动预测是实现快速、反应式且安全的机器人避障导航的关键。