Joint Motion Planning

Joint motion planning focuses on coordinating the movements of multiple agents, such as robots or vehicles, to achieve a common goal, optimizing factors like efficiency, resource allocation, and collision avoidance. Current research emphasizes developing distributed algorithms, often leveraging techniques like submodular optimization and reinforcement learning (including deep inverse reinforcement learning), to handle the computational complexity of coordinating numerous agents in real-time. These advancements are crucial for applications ranging from multi-robot teams performing collaborative tasks to autonomous vehicles navigating complex environments, improving efficiency and safety in various domains.