Decentralized Multi Robot

Decentralized multi-robot systems aim to coordinate multiple robots collaboratively without a central controller, focusing on efficient task allocation, robust navigation, and reliable information fusion. Current research emphasizes developing algorithms leveraging deep reinforcement learning (DQN), control barrier functions, and graph neural networks to address challenges like collision avoidance, deadlock prevention, and maintaining connectivity in uncertain environments. These advancements are significant for improving the robustness and scalability of multi-robot systems, with applications ranging from environmental monitoring and search and rescue to industrial automation and swarm robotics.