Feedback Motion Prediction for Safe Unicycle Robot Navigation
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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. In this paper, as a more accurate 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 using a reference governor, where the safety of the unicycle motion is continuously monitored based on the predicted robot motion. We investigate the role of motion prediction on robot behaviour in numerical simulations and conclude that accurate feedback motion prediction is key for safe and fast robot navigation.
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