Decentralized Collision Avoidance and Formation Recovery for Multi-Robot Systems in Dynamic Environments: An AnyLogic-Based Evaluation

Kenneth Kayock Attans, Ahmed Abubakar Aliyu, Gabriel Lazarus Dams, Nehemiah Musa, Rimamtari David Nyatse

Abstract


Multi-agent robots can improve robustness, redundancy, and mission efficiency, but coordinated navigation becomes difficult when robots must preserve a formation while responding to static and moving obstacles. This study presents a decentralized framework for collision avoidance and formation control in a dynamic environment. The framework uses a leader–follower V-shaped formation in which the leader plans a route to the target with the A* algorithm, while each robot independently maintains its assigned bearing, spacing, and velocity relative to a preceding robot. Potential collisions are detected using Minimum Predicted Distance (MPD), and collision-risk regions are handled using a Minimum Enclosing Rectangle (MER) maneuver. Following avoidance, affected followers adjust speed, bearing, and spacing to return to the prescribed formation using the Proportional Integral Derivative (PID) control. The framework was implemented and evaluated in AnyLogic using an agent-based simulation containing 7, 10, 20, and 40 static and dynamic obstacles. Centralized and decentralized control were compared using mission duration, collision rate, mean formation error, path efficiency, obstacle-avoidance time, and command count. The results indicate that centralized control can be advantageous for smaller formations, whereas decentralized control becomes more scalable as the number of robots increases. The proposed architecture removes dependence on a central controller, reduces communication hops, and supports autonomous local responses in dynamic environments. 


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References


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