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Multi-robot task allocation for real-time hospital logistics

Autonomous mobile robots have been deployed at hospitals to cope with numerous delivery services. Robots deliver items from point to point through autonomous navigation. Conventional robots work individually conducting only one task at a time. This paper proposes a fleet optimization method to maximize the advantages of operating a group of robots concurrently by assigning multiple tasks to a robot. The server selects an appropriate robot for a delivery task based on the cost for conducting the delivery task of each robot. The robot with the minimum cost is assigned for the new delivery task. This way, a robot is assigned to conduct multiple tasks. The scheduler finds the minimum routing path for visiting multiple delivery locations. The shortest path for visiting multiple delivery locations is derived from a combinatorial search approach. This paper proposes an algorithm that reduces the computational burden for finding the path combinations. Because this procedure takes a short amount of time, the allocation of a robot can be conducted in real-time. The proposed algorithm is tested in the simulation, and the results show that it increases the efficiency when using multiple robots. In addition, by running the simulation and reviewing the performance results, the management group of a hospital can determine the number of robots required at their site.