Decentralized Market-Based Task Allocation Algorithm for a Fleet of Industrial Mobile Robots

In this paper, we present an efficient, resilient, and flexible market-based task allocation algorithm with a distributed architecture for a dynamic factory environment. The proposed algorithm provides efficient and intelligent task allocation mechanisms that reduce the time and total distance traveled by the agents. This algorithm is implemented in a simulation environment that is similar to a real-world environment with various robots and tasks to allocate to test its efficiency, resilience, and flexibility. It is compared quantitatively with other baseline solutions such as auction only with available robots and a queue system. The results show that the algorithm is more efficient than the other methods tested. It is also reliable since it can handle unpredictable behaviors such as corrupted messages, loss of connection for an extended period, failures to complete tasks, and obstacles blocking the robot's path and forcing them to take a different trajectory. Finally, it is flexible since it can be used for several different purposes and is robust to communications failures. Also, this algorithm possesses the drawback of being ill-equipped to manage a substantial influx of task requests, given that solely a single task is auctioned and assigned at any given time.