Title | ||
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A Novel Warehouse Multi-Robot Automation System With Semi-Complete And Computationally Efficient Path Planning And Adaptive Genetic Task Allocation Algorithms |
Abstract | ||
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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. |
Year | DOI | Venue |
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2018 | 10.1109/ICARCV.2018.8581092 | 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) |
DocType | Volume | ISSN |
Conference | abs/1809.07262 | 2474-2953 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kam Fai Elvis Tsang | 1 | 0 | 0.68 |
Yuqing Ni | 2 | 0 | 0.34 |
Cheuk Fung Raphael Wong | 3 | 0 | 0.68 |
Ling Shi | 4 | 1717 | 107.86 |