Feedback Motion Prediction for Safe Unicycle Robot Navigation

TL;DR

This paper introduces novel conic feedback motion prediction methods for unicycle robots, enabling fast and accurate safety assessment to improve reactive navigation around obstacles in cluttered environments.

Contribution

It proposes a new conic feedback motion prediction approach as an alternative to traditional Lyapunov level sets for unicycle robots, enhancing safety and reactivity.

Findings

Motion prediction improves safety in obstacle avoidance.

The method enables real-time safety monitoring during navigation.

Simulations confirm the effectiveness of the approach.

Abstract

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.