A Distributed Range-Only Collision Avoidance Approach for Low-cost Large-scale Multi-Robot Systems

Abstract

The challenges of developing low-cost, large-scale multi-robot navigation systems include noisy measurements, a large number of robots, and computing efficiency for collision avoidance. This paper presents a distributed motion planning framework for a large number of robots to navigate with robust collision avoidance using low-cost range only measurements. The novelty of this work is threefold. (1) Developing a distributed collision-free navigation system for a large-scale robot group in which each robot performs motion planning based on the noisy range measurements of neighboring robots; (2) Developing a set of algorithms for each robot to accurately estimate the relative positions and orientations based on the range measurements and relative velocities; (3) Developing a velocity obstacle (VO) based motion planning algorithm for each robot which can take into account of the estimation uncertainties in relative positions and orientations. The proposed approach is tested with various numbers of differential-driven robots in the Gazebo simulator and real-world experiments. Both simulation and experiment results validate the superior performance of the proposed approach compared to other state-of-art technologies.