Decentralized Path Planning

Decentralized path planning focuses on enabling multiple robots to navigate and achieve goals independently, without relying on a central controller, while avoiding collisions and optimizing overall efficiency. Current research emphasizes the development of scalable algorithms, such as graph neural networks and variations of belief propagation, to handle the complexity of multi-robot coordination in continuous spaces, often incorporating local communication constraints and online replanning strategies. This field is crucial for advancing multi-robot systems in various applications, from warehouse automation and search-and-rescue to human-robot collaboration, by improving efficiency, robustness, and scalability compared to centralized approaches.