A Traffic Avoidance Path Planning Method for Autonomous Vehicles in a Warehouse Environment

Autonomous mobile robots are widely used today in supply chain, manufacturing, and service systems. Major challenges in these systems are dispatching and path planning. Centralized systems typically assume the knowledge of location, planned path, status of all mobile robots in the system. This paper presents a simulation-based decentralized path planning method that has a traffic prediction and avoidance component. The method is applied to autonomous mobile robots in a warehouse environment. Given only the starting, pick-up, and drop-off locations of the other robots in the system, we utilize a deep-learning algorithm to predict heavy traffic zones with the goal of minimizing travel time. We conduct a Monte Carlo simulation analysis to demonstrate the capabilities of the method.