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We present multi-agent navigation methods for autonomous robots maneuvering in complex environments. Our approach considers the agent’s dynamics to compute smooth paths that avoid collisions with other agents and static and dynamic obstacles in a decentralized fashion. We assume each agent performs local sensing and has knowledge of its neighboring obstacles in its sensing region. For each agent, its local obstacle knowledge is used to construct collision avoidance constraints and is incorporated into an Model Predictive Control (MPC). The MPC considers the agent’s dynamics and the collision avoidance constraints to compute a smooth, locally optimal path for the agent. Additional task objectives, such as improved coverage, can be considered in the optimization problem for task-specific navigation. Real-world scenarios, such as military applications, can suffer from compromised sensing capabilities, resulting in noisy sensing data. Thus, we express the linear collision avoidance constraints as chance constraints and solve a probabilistic optimal control problem to improve safe operation. The chance constraints are reformulated as deterministic constraints assuming a Gaussian Mixture distribution. The computed control input safely navigates the agent toward its goal using the noisy sensing data. We compared the performance of our proposed methods with other state-of-the-art decentralized methods in dense scenarios with multiple obstacles. Our method produces smoother paths with improved collision avoidance performances even under noisy sensing. We observe that our method scales to tens of agents owing to its decentralized computation.
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