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

CSR：小型：协作研究：使用空中机器人对传感器网络进行自适应和自主能源管理

• 批准号: 1217400
• 负责人: Carrick Detweiler
• 金额: $ 39万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217400&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Adaptive

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.

这项研究引入了新颖的充电系统和算法来补充现有系统，并在传感器网络上实现自主、可持续的能源管理。 桥梁故障检测等依赖于远离建筑物运行的传感器网络的应用通常缺乏用于长期监控的能源。 在这些情况下，传统的充电方法（例如太阳能电池板）不可用或无法提供足够的能量（例如在夜间）。 为了实现这一目标，该研究包含三个关键组成部分。 第一个组件开发了一种对传感器节点进行无线充电的无人机（UAV）、基于磁场强度的定位系统以及优化电力传输的自主控制算法。 基于该系统，第二部分开发了在具有多个无人机的系统中用于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高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。这项研究影响了一系列社会、教育和科学主题。 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。 鉴于基础设施老化，并且通过当前的手动目视检查流程执行此类工作的当地预算有限，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来增强现有项目，鼓励年轻学生（尤其是女性）追求 STEM 领域。