CSR: Small: Collaborative Research: Adaptive and Autonomous Energy Management on a Sensor Network Using Aerial Robots

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This research introduces novel recharging systems and algorithms to supplement existing systems and lead to autonomous, sustainable energy management on sensor networks. Applications such as bridge fault detection that rely on sensor networks operating away from buildings often lack energy for long-term monitoring. In these scenarios traditional recharging methods (e.g. solar panels) are unavailable or cannot provide sufficient energy (e.g. at night). To achieve this, the research has three key components. The first component develops an unmanned aerial vehicle (UAV) to wirelessly recharge sensor nodes, a localization system based on magnetic field intensity, and autonomous control algorithms that optimize power transfer. Based on this system, the second part develops online algorithms for UAV trajectory optimization in systems with multiple UAVs as well as distributed algorithms that induce energy deficits in a subset of nodes to enable efficient recharging. Finally, the third component provides models and distributed algorithms to predict future energy recharging and usage for sensor nodes.This research impacts a range of societal, educational, and scientific topics. This project addresses a critical national need of enabling regular monitoring of bridge integrity through autonomous, energy efficient sensor network and robot systems. Improving bridge fault detection is necessary given aging infrastructure and limited local budgets for performing such work through current manual visual inspection processes. Educationally, at the K-12 level, this provides demos and workshops to enhance existing programs, encouraging young students (especially women) to pursue STEM fields.