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We consider the problem of warehouse multi-robot automation system indiscrete-time and discrete-space configuration with focus on the taskallocation and conflict-free path planning. We present a system design where acentralized server handles the task allocation and each robot performs localpath planning distributively. A genetic-based task allocation algorithm isfirstly presented, with modification to enable heuristic learning. Asemi-complete potential field based local path planning algorithm is thenproposed, named the recursive excitation/relaxation artificial potential field(RERAPF). A mathematical proof is also presented to show the semi-completenessof the RERAPF algorithm. The main contribution of this paper is themodification of conventional artificial potential field (APF) to besemi-complete while computationally efficient, resolving the traditional issueof incompleteness. Simulation results are also presented for performanceevaluation of the proposed path planning algorithm and the overall system.

A Novel Warehouse Multi-Robot Automation System with Semi-Complete and  Computationally Efficient Path Planning and Adaptive Genetic Task Allocation  Algorithms