Multi Robot Task

Multi-robot task allocation focuses on efficiently assigning tasks to teams of robots to achieve collective goals, optimizing factors like task completion time, energy consumption, and collision avoidance. Current research emphasizes developing robust algorithms, including reinforcement learning, auction-based methods, and optimization techniques (e.g., mixed-integer linear programming, satisfiability modulo theories), often incorporating models of trust, robot capabilities, and environmental uncertainties (e.g., using hypergraphs or graph neural networks). This field is crucial for advancing autonomous systems in diverse applications such as warehouse logistics, search and rescue, and environmental monitoring, where efficient coordination of multiple robots is essential for successful task completion.