On the design for AGVs: Modeling, path planning and localization

Abstract

Intelligent warehouse becomes a key component of logistics process automation, which essentially promotes the productivity and cost reduction. This paper presents a novel design solution of an Automated Guided Vehicles (AGVs) system for intelligent warehouse. An improved version of classical Dijkstra shortest-path algorithm is proposed for efficient global path planning. In the case of multi-AGV, the time windows method is used to address the issue of conflict and deadlock. In addition, the local path planning and auto-localization is addressed by using a heuristics-based algorithm and Monte Carlo Localization algorithm respectively. Extensive numerical experiments based on Player/Stage simulator are carried out to assess the suggested algorithms, for a range of scenarios and the result well validates its effectiveness. Currently the proposed design solution is adopted in developing the prototype of AGVs to be deployed in practice.