Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams
This addresses the need for robust and fast coordination in multi-robot systems for applications like greenhouse maintenance, though it appears incremental as it builds on existing distributed methods.
The paper tackles the problem of coordinating multi-robot teams with tightly coupled tasks by presenting a distributed task allocation and scheduling algorithm based on evolutionary computation, which achieves better computational speed and scalability without loss of optimality compared to state-of-the-art methods.
To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.