Vision-Based Collision Avoidance for Multi-Agent Systems with Intermittent Measurements

Collision avoidance is a fundamental aspect of many applications involving autonomous vehicles. Solving this problem becomes especially challenging when the agents involved cannot communicate. In these scenarios, onboard sensors are essential for detecting and avoiding other vehicles or obstacles. However, in many practical applications, sensors have limited range and measurements may be intermittent due to external factors. With this in mind, in this work, we present a novel decentralized vision-based collision avoidance algorithm which does not require communication among the agents and has mild assumptions on the sensing capabilities of the vehicles. Once a collision is detected, the agents replan their trajectories to follow a circular path centered at the point of collision. A feedback control law is designed so that the vehicles can maintain a predefined phase shift along this circle and therefore are able to avoid collisions. A Lyapunov analysis is performed to provide performance bounds and the efficacy of the proposed method is demonstrated through both simulated and experimental results.