AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm

Autonomous-guided vehicles (AGVs) are becoming increasingly prevalent and already helping in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors and microelectromechanical systems are bringing us closer to the realization of autonomous multi robot systems which perform complex tasks. In this work, we explored the problem of vehicle routing and motion planning for a fleet of AGVs in a flexible manufacturing system (FMS). Considering concurrently fuzzy demands associated with the workstations and fuzzy travel distances while moving between workstations, the problem is addressed in the context of uncertainty both in demands and travel times. The proposed motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. Furthermore, in order to take into account the fuzziness which may arise in the FMS, the proposed planner is integrated with fuzzy theory on fuzzy sets and fuzzy numbers. The efficiency of the solution procedure is demonstrated through numerical examples.