Optimal Trajectory and Schedule Planning for Autonomous Guided Vehicles in Flexible Manufacturing System

This paper proposed an algorithm to solve major challenges in the domain of autonomous transportation systems. These challenges are trajectory planning and collision avoidance. Generally, in a shared infrastructure where several agents aim to use limited capacity resources, finding a set of optimal and conflict-free paths for each single agent is the most critical part. In such a dynamic environment where continual planning and scheduling are required, we have adopted a coupled (centralized) technique and face the problem by breaking it into two distinct phases: path planning and collision avoidance. In the first phase, the lowest cost path is planned for each agent by a modified version of Dijkstra algorithm. Following that, in the second phase, the head-on collisions can be foreseen and avoided by detecting the agents' common segments of the path choices. Finally, based on the capacity of each resource and the feasibility of each path, the task assignment and scheduling algorithm allocates different tasks between agents.