Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection

Abstract

• 5 agents are equipped with a dedicated sensor to find buried Improvised Explosive Devices (IED). • Agents have our originally designed AI enabling them to work independently and classify buried IED. • their decisions and positions are shared to also generate a collective (cooperative) behavior. • shared information is then aggregated in each agent to make a more accurate classification. • A high number of AI systems are designed, tested in our originally built robotic platform, and compared. This work presents a novel intelligent system designed using a multi-agent hardware platform to detect improvised explosive devices concealed in the ground. Each agent is equipped with a different sensor, (i.e. a ground-penetrating radar, a thermal sensor and three cameras each covering a different spectrum) and processes dedicated AI decision-making capabilities. The proposed system has a unique hardware structure, with a distributed design and effective selection of sensors, and a novel multi-phase and cooperative decision-making framework. Agents operate independently via a customised logic adjusting their sensor positions - to achieve optimal acquisition; performing a preliminary “local decision-making” - to classify buried objects; sharing information with the other agents. Once sufficient information is shared by the agents, a collaborative behaviour emerges in the so-called “cooperative decision-making” process, which performs the final detection. In this paper, 120 variations of the proposed system, obtained by combining both classic aggregation operators as well as advanced neural and fuzzy systems, are presented, tested and evaluated. Results show a good detection accuracy and robustness to environmental and data sets changes, in particular when the cooperative decision-making is implemented with the neuroevolution paradigm.