Distributed Intelligent Formation Control for Mobile Robots Via Multisource Perception

This article proposes a distributed intelligent formation control method for mobile robots via multisource perception. Multirobot formation control is challenging in unknown environments due to the lack of global pose information and the susceptibility of visual tags to disturbances. To cope with the constraints imposed by data, a novel deep reinforcement learning (DRL) flexible formation motion control framework is constructed. The followers compute motion decisions based on local multisource perception data, autonomously achieving shape formation and formation maintenance. Each robot optimizes the formation by predicting the pose of its leader and reduces the average path length of the formation. This framework designs a multistage DRL incremental training method for pursuit-evasion scenarios, significantly enhancing the robustness and adaptability of the formation system. Simulation and physical experiments show that the proposed formation control method not only adapts to complex unknown environments, but also autonomously achieves formation recovery in scenarios involving member detachment and formation disruption.