ERI: Fault-Tolerant Monitoring of Moving Clusters of Targets using Collaborative Unmanned Aerial Vehicles

ERI：使用协作无人机对移动目标群进行容错监控

• 批准号: 2301707
• 负责人: Gustavo Vejarano
• 金额: $ 19.7万
• 依托单位: Loyola Marymount University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301707&HistoricalAwards=false
• 关键词: ERI; Fault; Tolerant; Monitoring; Moving

Unmanned aerial vehicles, or drones, have successfully been used to monitor ground activity. However, using small drones for extended periods of time is not yet possible, thus limiting their implementation. For instance, small quadcopters that can be easily transported and deployed do not exceed forty minutes of flying time in most cases and are susceptible to unexpected failure such as damage from natural hazards. On the other hand, robust quadcopters capable of longer flying times have larger dimensions and weight that prohibit ease of deployment. As an alternative to a single robust drone, this Engineering Research Initiation (ERI) award will support fundamental research to enable a network of small drones to monitor ground activity with the goal of uninterrupted operation and fault tolerance by sharing information with one another including knowledge of targets detected. A demonstration of this concept will be made through wildfire monitoring in collaboration with the US Forest Service. This award will sustain research at a predominantly undergraduate institution. Both undergraduate and graduate students will participate in the research effort.The monitoring problem under consideration in this research is related to the well-known multiple traveling salesman problem and its variants, namely Vehicle Routing Problem with Time Window and Multiple Depot Drone Routing Problem. However, the solution to these routing problems cannot be used as-is because they would consider drone tours that visit each cluster (region) only once, not periodically. Moreover, they do not consider fault tolerance. This research aims at a fault-tolerant solution that (1) characterizes target clusters via distributed estimation of Gaussian Mixture Models, (2) coordinates flight formations and search paths using game theory to avoid central control, and (3) performs point set registration of adjacent images from drones to increase accuracy of target locations. The research will not only promote progress in wildfire monitoring but also for any other natural or human activity that can be characterized with Gaussian Mixture Models.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无人驾驶汽车或无人机已成功用于监视地面活动。但是，不可能长时间使用小型无人机，从而限制了它们的实施。例如，在大多数情况下，可以轻松运输和部署的小四四足动物不超过四十分钟的飞行时间，并且容易受到意外故障的影响，例如造成自然危害的损害。另一方面，能够更长的飞行时间的稳健四肢杆具有更大的尺寸和重量，可以禁止部署。作为单个强大无人机的替代方案，该工程研究启动（ERI）奖将支持基本研究，以使小型无人机网络通过彼此共享信息，包括对被检测到的目标的知识，以不间断的操作和容忍度来监视地面活动。该概念的演示将通过与美国森林服务部合作进行野火监控。该奖项将在主要的本科机构中维持研究。本科生和研究生都将参与研究工作。本研究中正在考虑的监测问题与众所周知的多个旅行者问题及其变体有关，即带有时间窗口的车辆路由问题和多个仓库无人机路由问题。但是，解决这些路由问题的解决方案无法使用，因为它们会考虑仅访问每个集群（区域）一次，而不是定期访问每个集群的无人机旅行。而且，他们不考虑容错。这项研究的目的是（1）通过对高斯混合模型的分布式估计来表征目标簇，（2）使用游戏理论协调飞行形态和搜索路径以避免中心控制，并且（3）执行从无人机中的邻近图像的点设置注册，以提高目标位置的准确性。这项研究不仅将促进野火监测的进展，而且还可以促进任何可以以高斯混合模型为特征的自然或人类活动。该项目得到了机器人技术计划中的跨导向基础研究的支持，并共同管理和资助了由工程局（ENG）和计算机和信息科学和工程（CISE）进行的，并以nsf的指定为基础。智力优点和更广泛的影响审查标准。