Multi Agent Pickup

Multi-agent pickup and delivery (MAPD) focuses on efficiently coordinating multiple agents to complete pickup and delivery tasks, minimizing conflicts and optimizing performance metrics like delivery time and energy consumption. Current research emphasizes developing robust algorithms, such as those based on token passing, conflict-based search, and reinforcement learning, to handle dynamic environments, deadlines, and unexpected disruptions, often incorporating advanced techniques like task swapping and environment manipulation ("terraforming"). These advancements have significant implications for optimizing logistics in automated warehouses, factories, and other multi-agent systems, improving efficiency and throughput while addressing challenges like deadlock avoidance and equitable workload distribution.