Centralized and optimal motion planning for large-scale AGV systems: A generic approach. (April 2017) - Li, Bai; Liu, Hong; Xiao, Duo; Yu, Guizhen; Zhang, Youmin
Highlights: A centralized motion planner is proposed for large-scale AGV systems; AGV kinematics and dynamics are clearly incorporated into the problem formulation; Formation reconfiguration benchmark cases are set up. Validation and unification of the proposed motion planner are investigated via exhaustive tests; On-line computation capability is promising with near-optimal initial guess. Abstract: A centralized multi-AGV motion planning method is proposed. In contrast to the prevalent planners with decentralized (decoupled) formulations, a centralized planner contains no priority assignment, decoupling, or other specification strategies, thus is free from being case-dependent and deadlock-involved. Although centralized motion planning is computationally expensive, it deserves investigations in schemes that are sensitive to solution quality but insensitive to computation time. Specifically, centralized multi-AGV motion planning is formulated as an optimal control problem in this work, wherein differential algebraic equations are used to describe the AGV dynamics, mechanical restrictions, and exterior constraints. Orthogonal collocation direct transcription method is adopted to discretize the original infinite-dimensional optimal control problem into a large-scale nonlinear programming (NLP) problem, which is solved using interior point method thereafter. Exhaustive simulations are conducted on 10-AGV formation reconfiguration tasks. Simulation results show the validation, unification, and real-time implementation potential of the introduced centralized planner. Particularly, the computation time on a PC reduces to several seconds with near-optimal initial guess in the NLP solving process, making receding horizon replanning possible via this centralized planner.