Development of a Fleet Management System for Multiple Robots' Task Allocation Using Deep Reinforcement Learning

This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system's effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics.