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

本研究介绍了新的充电系统和算法，以补充现有的系统，并导致传感器网络的自主，可持续的能源管理。 诸如桥梁故障检测等依赖于远离建筑物的传感器网络的应用通常缺乏长期监测的能量。 在这些情况下，传统的充电方法（例如太阳能电池板）不可用或不能提供足够的能量（例如在夜间）。 为了实现这一目标，研究有三个关键组成部分。 第一个组成部分开发一种无人机（UAV），用于无线充电传感器节点，基于磁场强度的定位系统，以及优化功率传输的自主控制算法。 基于该系统，第二部分开发了用于多无人机系统中的无人机轨迹优化的在线算法，以及在节点子集中引起能量不足以实现有效充电的分布式算法。 最后，第三部分提供模型和分布式算法来预测传感器节点未来的能量充电和使用。 该项目满足了国家的一项关键需求，即通过自主、节能的传感器网络和机器人系统定期监测桥梁的完整性。 由于基础设施老化，而且通过目前的人工目视检查过程执行此类工作的地方预算有限，因此有必要改进桥梁故障检测。 在教育方面，在K-12级别，这提供了演示和研讨会，以加强现有的计划，鼓励年轻学生（特别是女性）追求STEM领域。

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.