Cooperative Transportation

Cooperative transportation research focuses on enabling multiple robots, ranging from microrobots to quadrotors and mobile manipulators, to collaboratively transport objects exceeding individual capabilities. Current efforts concentrate on developing robust control strategies, often employing model predictive control, reinforcement learning (including multi-agent reinforcement learning and counterfactual reward schemes), and distributed algorithms to handle dynamic environments, communication delays, and uncertainties in object properties or robot failures. This field is significant for advancing automation in logistics, construction, and other domains requiring efficient and flexible material handling, particularly in challenging or unstructured environments.