A Route Planning Method for Multiple Mobile Robots by Combining Deep Q-Network and Graph Search

In recent years, automated multiple mobile robots are introduced for transporting luggage and inspecting final products in factories to reduce the burden of human labor shortage. There is a need for mobile robots to develop autonomous systems that make flexible decisions like human operators. We propose a route planning method that combines deep reinforcement learning and graph search methods. In the proposed method, the routing is firstly determined by a graph search algorithm, and deep Q-network (DQN). A deep reinforcement learning method is used to avoid collisions. The proposed method is applied to the multiple drones route planning problem. As a result, a near-optimal routing is obtained that can reach to the destination while avoiding collisions between drones. These results suggest that the route planning problem in a three dimensional environment is successfully solved by using DQN that can process multidimensional states. We generate a learning model for collision avoidance using DQN for both whole observation and partial observation ranges to verify the usefulness of path planning with partial observations through computational experiments.