Intelligent Data Fusion and Multi-Agent Coordination for Target Allocation

This paper addresses the fusion processing techniques for multi-sensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. To combine the multi-sensor data from the distributed battlefield robots, a set of fusion rules are used to formulate a combined prediction from the multi-source data. The possible type of a target is estimated through the fusion rules. For the identification of targets, agents need to keep track of targets for continuous situation awareness. The coordination of the agents with limited range of surveillance is indispensable for their successful monitoring of multiple targets. For dynamic and flexible coordination, our agents follow the decision-theoretic approach. We implement a military simulator to compare the capabilities of fusion processing and those of coordination, and conduct experiments with our framework in distributed and uncertain battlefield environments. The experimental results show that the fusion process of multi-sensor data from military robots can improve the performance of estimation of the type of a target, and our coordinated agents outperform agents using random strategy for their target selection in various military scenarios.