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：作为简单且鲁棒的移动机器人基座，可建模为运动学单轮模型的差速驱动机器人在工业与家庭环境中的物流及服务机器人领域具有重要应用。在复杂杂乱环境中（尤其是人群及其他机器人周围）实现安全避障导航，是该类单轮机器人执行多样化实用任务的核心能力。快速且精确的安全评估对于反应式安全运动设计至关重要。本文提出一种新颖的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具简洁性的替代方案，用于界定标准单轮运动控制方法下的运动学单轮机器人闭环运动轨迹。我们展示了基于参考管理器的单轮机器人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。通过数值仿真研究运动预测对机器人行为的影响，得出结论：快速精确的反馈运动预测是实现高速、反应式且安全避障导航的关键。