Local reactive robot navigation: A comparison between reciprocal velocity obstacle variants and human-like behavior

Abstract

Most local robot navigation algorithms are based on the concept of velocity obstacle, a mechanistic approach to the navigation problem in which a solution is engineered from scratch. Over the years, a number of different velocity obstacle variants have been developed to effectively handle multi-robot systems. In parallel, an alternative, human-inspired approach for robot navigation has been recently proposed, which derives from the observation and modeling of crowds of pedestrians. We discuss similarities and differences among two broadly used obstacle-velocity variants, namely Hybrid Reciprocal Velocity Obstacle and Optimal Reciprocal Collision Avoidance, and the human-inspired approach. How do these differences (which are often subtle) impact performance, and why? We answer these questions through extensive simulation experiments, wherein we evaluate the the algorithms for safety, trajectory efficiency, and emergence of collective behaviors, in different challenging multi-robot scenarios using both ideal and realistic models for robots and sensing.