Multi Robot Problem

Multi-robot problems focus on coordinating multiple robots to achieve a shared objective, addressing challenges like scalability, decentralized control, and efficient information sharing. Current research emphasizes developing robust and scalable algorithms, including permutation-invariant neural networks for adaptable control, physics-informed reinforcement learning for energy-efficient cooperation, and distributed optimization methods for efficient task allocation and resource management. These advancements are crucial for improving the performance and reliability of multi-robot systems in diverse applications, such as industrial automation, search and rescue, and environmental monitoring.