Collaborative Research: An Adaptive Dynamic Programming Approach to the Coordination of Heterogeneous Robotic Sensors Networks

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The objective of this research is to develop adaptive coordination and control methods for autonomous sensor networks employed for search and rescue operations, humanitarian demining, and ambient monitoring. The approach is to develop adaptive dynamic programming algorithms for this new class of hybrid systems, to optimize their performance over time by coordinating and implementing future sensor actions intelligently, based on prior knowledge and information obtained by the sensors online.Intellectual meritThis research develops novel hybrid adaptive dynamic programming theory and algorithms for robotic sensor networks. Online learning and adaptive control are crucial to robotic sensor networks because they are, by necessity, deployed in highly unstructured and uncertain environments, with little or no prior information about the obstacles or the targets. This research will merge recent interdisciplinary developments in computer science, robotics, and engineering to develop a novel system-theoretic approach that integrates hybrid control with computational geometry algorithms, and optimizes objective functions derived using information and probability theories.Broader impactBy optimizing the performance of emerging sensor technologies and increasing their level of autonomy, this research will enable them to remove or assist humans in carrying out dangerous yet vital missions, such as humanitarian demining, and rescue efforts following hurricanes, fires, or avalanches. Research results will be disseminated and demonstrated on real systems through existing collaborations with the industry and international institutions. Students will be recruited from underrepresented minorities at both institutions, Duke and UNM, to participate in stimulating education activities, including robotic games, computer game competitions, and environmental research.