Please use this identifier to cite or link to this item:
Title: A bi-level probabilistic path planning algorithm for multiple robots with motion uncertainty
Authors: Wang, Jingchuan
Tai, Ruochen
Xu, Jingwen
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Wang, J., Tai, R., & Xu, J. (2020). A bi-level probabilistic path planning algorithm for multiple robots with motion uncertainty. Complexity, 2020, 9207324-. doi:10.1155/2020/9207324
Journal: Complexity 
Abstract: For improving the system efficiency when there are motion uncertainties among robots in the warehouse environment, this paper proposes a bi-level probabilistic path planning algorithm. In the proposed algorithm, the map is partitioned into multiple interconnected districts and the architecture of proposed algorithm is composed of topology level and route level generating from above map: in the topology level, the order of passing districts is planned combined with the district crowdedness to achieve the district equilibrium and reduce the influence of robots under motion uncertainty. And in the route level, a MDP method combined with probability of motion uncertainty is proposed to plan path for all robots in each district separately. At the same time, the number of steps for each planning is dependent on the probability to decrease the number of planning. The conflict avoidance is proved, and optimization is discussed for the proposed algorithm. Simulation results show that the proposed algorithm achieves improved system efficiency and also has acceptable real-time performance.
ISSN: 1076-2787
DOI: 10.1155/2020/9207324
Rights: © 2020 Jingchuan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
9207324.pdf2.65 MBAdobe PDFView/Open

Citations 50

Updated on Jan 19, 2023

Web of ScienceTM
Citations 50

Updated on Jan 23, 2023

Page view(s)

Updated on Jan 29, 2023

Download(s) 50

Updated on Jan 29, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.