Velocity obstacle approaches for multi-agent collision avoidance

This paper presents a critical analysis of some of the most promising approaches to geometric collision avoidance in multi-agent systems namely; the velocity obstacle (VO), reciprocal velocity obstacle (RVO), hybrid-reciprocal velocity obstacle (HRVO) and optimal reciprocal collision avoidance (ORCA) approaches. Each approach is evaluated with respect to increasing agent populations and variable sensing assumptions. An intensive 1000 cycle Monte Carlo analysis is used to assess the performance of the selected algorithms in the presented conditions. The optimal reciprocal collision avoidance (ORCA) method is shown to yield the most scalable computation times and collision likelihood in different testing scenarios. The respective features and limitations of each algorithm are discussed and presented through examples.