Distributed Navigation with Dynamic Obstacles

One of the key challenges for multi-agent systems is collision free navigation in an unknown environment. In this work, we propose a unified framework for joint localization, control, and collision avoidance of multi-agent systems navigating in an unknown environment in the presence of dynamic obstacles. The cooperative agents rely on information from immediate neighboring agents within their communication neighborhood, and the dynamic obstacles are modelled as non-cooperative agents. The agents achieve localization by exploiting the individual agent dynamics, and pairwise distance measurements with agents in the sensing neighborhood of each cooperative agent. To ensure collision-free navigation, we exploit a Model Predictive Control (MPC) for each agent, with avoidance constraints using safety radius between pairwise agents. Futhermore, to avoid single point of failure, we propose Cooperative Positioning, Control and Collision Avoidance (CPCCA), which is based on distributed Method of Multipliers methods. We validate our framework and algorithms through simulations, demonstrating its effectiveness in real world scenarios, and propose directions for future work.