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Receding horizon path planning of automated guided vehicles using a time‐space network model
Author(s) -
Xin Jianbin,
Wei Liuqian,
Wang Dongshu,
Xuan Hua
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2654
Subject(s) - time horizon , computer science , motion planning , path (computing) , horizon , mathematical optimization , space (punctuation) , collision avoidance , operations research , collision , artificial intelligence , engineering , mathematics , geometry , computer security , robot , operating system , programming language
Summary Time‐space network (TSN) models have been widely used for collision‐free path planning of automated guided vehicles. However, existing TSN models are planned globally. The global method suffers from computational complexity and uncertainties cannot be dealt with in the dynamic environment. To address these limitations, this article proposes a new methodology to decompose the global planning problem into smaller local planning problems, which are planned in a receding horizon way. For the local problem, new decision variables and constraints are incorporated into the TSN framework. Extensive simulation experiments are carried out to show the potential of the proposed methodology. Simulation results show that the proposed method obtains competitive performances and computational times are considerably reduced, compared with the global method.