Abstract
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.
Highlights
The study of multi-sensor data fusion is a major area of research in distributed multi-agent systems, sensor networks, wearable computing, and fault detection [1,2,3], since this fusion unifies multiple sources of data into a unique and accurate picture of output
We proposed a set of fusion operators to combine multi-sensor data from military robots and a framework of coordinated target allocation for surveillance robot agents, and implemented a scenario-based simulator to repeatedly assess the intelligent fusion process and the coordinated target selection in battlefield environments
Military surveillance robots search for possible threats among some targets
Summary
The study of multi-sensor data fusion is a major area of research in distributed multi-agent systems, sensor networks, wearable computing, and fault detection [1,2,3], since this fusion unifies multiple sources of data into a unique and accurate picture of output. Battlefield surveillance robots equipped with infrared sensors closely monitor moving targets These military robots are semi-autonomously operated; that is, their actions are mostly decided by themselves, but sometimes controlled by their commanders. A decision-theoretic framework is here proposed as a method for the overall correspondence among robots and targets This approach endows surveillance robots with flexible and dynamic target selection for their real-time coordination in multi-agent environments. The commander at the control center provides feedback on the estimations of the types of multiple robots based upon the results of fusion processing and the best target allocation coordinated with other robots. The final section summarizes our results and discusses further research issues
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
