Receding horizon path planning of automated guided vehicles using a time‐space network model. (17th August 2020)

• Record Type: Journal Article
• Authors: Xin, Jianbin; Wei, Liuqian; Wang, Dongshu; Xuan, Hua
• Other Names: Yuan Shuai guestEditor.; Wan Yimin guestEditor.; Zhang Lixian guestEditor.; Baldi Simone guestEditor.
• Is Part Of: Optimal control applications and methods. Volume 41:Number 6(2020)
• Page Start: 1889
• Page End: 1903
• Publication Date: 2020-08-17
• Subjects: automated guided vehicle -- path planning -- receding horizon planning -- time‐space network model; Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2654
• Languages: English
• ISSNs: 0143-2087

Abstract

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.