Efficient multi-robot exploration of unknown environments using inverted ant colony optimization and reinforcement learning

Collaborative environmental exploration by a fleet of mobile robots is of growing interest, especially in the context of unknown environments. Exploration algorithms find diverse and critical applications, such as search and rescue, underwater surveillance, and space observation. However, despite significant advances in the field, a persistent gap between research results and their translation into real-world applications is a major obstacle to the deployment of effective solutions. This paper proposes a hybrid approach, called IACO-RL, which combines inverse ant colony optimization (IACO) with reinforcement learning (RL) to improve exploration efficiency. This method aims to maximize space coverage and minimize exploration time, with the additional goal of accurately locating mines hidden in the environment. The IACO algorithm directs robots to scarce or unexplored areas by reversing the classical pheromone deposition mechanism, thus promoting efficient spatial dispersal. For its part, the RL module allows each agent to learn autonomously from its interactions with the environment, thus enhancing its adaptability and local decision-making capacity. Experimental results, obtained through simulations in different environmental scenarios, show that the IACO-RL approach outperforms single methods in terms of coverage, speed and mine detection capacity. These performances confirm the relevance of this hybridization and highlight that effective mine detection results directly from the efficiency of the exploration performed by the multi-robot system.