Cooperative Formation Control of a Multi-Agent Khepera IV Mobile Robots System Using Deep Reinforcement Learning

Abstract

The increasing complexity of autonomous vehicles has exposed the limitations of many existing control systems. Reinforcement learning (RL) is emerging as a promising solution to these challenges, enabling agents to learn and enhance their performance through interaction with the environment. Unlike traditional control algorithms, RL facilitates autonomous learning via a recursive process that can be fully simulated, thereby preventing potential damage to the actual robot. This paper presents the design and development of an RL-based algorithm for controlling the collaborative formation of a multi-agent Khepera IV mobile robot system as it navigates toward a target while avoiding obstacles in the environment by using onboard infrared sensors. This study evaluates the proposed RL approach against traditional control laws within a simulated environment using the CoppeliaSim simulator. The results show that the performance of the RL algorithm gives a sharper control law concerning traditional approaches without the requirement to adjust the control parameters manually.