2018/09/19 by Kam Fai Elvis Tsang, Yuqing Ni, Tsang, Kam Fai Elvis +5
Computer Science · Engineering · #Advanced Manufacturing and Logistics Optimization #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Multiagent Systems (cs.MA) #Robotic Path Planning Algorithms
paper · pdf · doi:10.48550/arxiv.1809.07262
openalex publication_date 2018/09/19 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
We consider the problem of warehouse multi-robot automation system in discrete-time and discrete-space configuration with focus on the task allocation and conflict-free path planning. We present a system design where a centralized server handles the task allocation and each robot performs local path planning distributively. A genetic-based task allocation algorithm is firstly presented, with modification to enable heuristic learning. A semi-complete potential field based local path planning algorithm is then proposed, named the recursive excitation/relaxation artificial potential field (RERAPF). A mathematical proof is also presented to show the semi-completeness of the RERAPF algorithm. The main contribution of this paper is the modification of conventional artificial potential field (APF) to be semi-complete while computationally efficient, resolving the traditional issue of incompleteness. Simulation results are also presented for performance evaluation of the proposed path planning algorithm and the overall system.