Towards Efficient Distributed Collision Avoidance for Heterogeneous Mobile Robots

We study the problem of distributed collision avoidance for mobile robotic systems, where a group of heterogeneous robots with different sizes and motion constraints avoid collisions with each other and static obstacles during the movements from their starting to goal locations. Existing methods mainly consider homogeneous robots and incur a high collision rate in environments with moving robots and static objects. Hence, we propose a distributed collision avoidance for heterogeneous mobile robots (Heter-CA), which allows each robot to independently avoid collisions considering the heterogeneity of robots and varying static obstacles. In Heter-CA, each robot predicts the trajectories of neighboring robots and estimates the varying size of static obstacles with the robots' range-finder sensors before motion planning, which enables each robot to avoid obstacles safely. Besides, we prove that Heter-CA can guarantee collision-free movement between heterogeneous robots by satisfying sufficient conditions. We evaluate Heter-CA in numerous simulated and real-world scenarios in which groups of heterogeneous robots perform navigation tasks. The experimental results demonstrate that Heter-CA takes 10times10× less computation time and achieves 5% less collision rate than baseline algorithms.