NeTS: Small: Co-Optimization of Sensing, Communications and Navigation of a Robotic Network under Resource Constraints

NeTS：小型：资源约束下机器人网络的传感、通信和导航协同优化

• 批准号: 1321171
• 负责人: Yasamin Mostofi
• 金额: $ 40万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321171&HistoricalAwards=false
• 关键词: NeTS; Small; Co; Optimization; Sensing

Robotic networks can have a tremendous impact in many different areas such as disaster relief, emergency response, and national security. The recent disasters such as Hurricane Sandy of 2012 or Japan's earthquake of 2011 remind us of the crucial role that unmanned autonomous networks can play as part of our society. The goal of this project is to introduce a new multi-disciplinary design paradigm for the successful operation of mobile robotic networks through the co-optimization of sensing, communications and navigation. In the robotics/control community, most existing work does not deal with realistic communication issues (such as shadowing and multipath fading) and ideal links/disk models are assumed for predicting connectivity. On the other hand, the communication and networking communities are not typically concerned with path planning and navigation. In a robotic network, path planning not only affects sensing quality but also impacts connectivity maintenance. This multi-disciplinary nature makes designing robust decision-making strategies for a successful task accomplishment in robotic networks considerably challenging and an open problem. Furthermore, a separate optimization of the given sensing, communications and navigation resources may not suffice for a successful operation under resource constraints.In this research effort, the focus is on the impact of limited energy (both motion and communications), time, and bandwidth resources and on laying the foundation of the corresponding optimum sensing, communication and navigation co-design policies, which includes trajectory, sensing, connectivity, motion speed/power, and communication transmission rate/power optimization. In this approach, realistic probabilistic connectivity metrics are properly co-optimized with sensing and navigation goals such that each robot chooses a trajectory that allows it to maximize its information gathering while maintaining the needed connectivity. This framework answers fundamental questions such as when to invest in motion and when to invest in communications. The project also addresses task feasibility and the fundamental limits of information generation, gathering and exchange, which can provide key insights for resource planning before deployment. Overall, this new co-optimization foundation enables the successful operation of robotic networks under limited resources and can thus have a tremendous impact on our society. This project also has a significant educational impact on minority and under-represented students.

机器人网络在许多不同的领域都会产生巨大影响，例如救灾，应急响应和国家安全。最近的灾难，例如2012年飓风桑迪（Sandy）或2011年日本地震，使我们想起了无人自主网络可以作为我们社会的一部分发挥的关键作用。该项目的目的是引入一个新的多学科设计范式，以通过感应，通信和导航的合作进行移动机器人网络成功运行。在机器人/控制社区中，大多数现有的工作都不涉及现实的通信问题（例如阴影和多路径褪色），并且假定理想的链接/磁盘模型用于预测连接。 另一方面，通信和网络社区通常与路径计划和导航有关。在机器人网络中，路径规划不仅会影响感应质量，还会影响连接维护。这种多学科的性质使设计强大的决策策略为在机器人网络中取得成功的任务成就而实现的强大决策策略非常具有挑战性和开放的问题。此外，对给定感应，通信和导航资源的单独优化对于在资源约束下成功运营可能不足资源并为相应的最佳感应，通信和导航共同设计策略奠定基础，其中包括轨迹，传感，连通性，运动速度/功率以及通信传输速率/功率优化。在这种方法中，现实的概率连通性指标可以通过感应和导航目标正确地优化，从而使每个机器人都选择一个轨迹，使其能够在维持所需的连接性的同时最大程度地提高其信息收集。该框架回答了基本问题，例如何时投资运动以及何时投资通信。 该项目还解决了任务可行性以及信息生成，收集和交换的基本限制，这可以在部署前为资源计划提供关键见解。 总体而言，这个新的合作基金会使机器人网络在有限的资源下成功运营，因此可以对我们的社会产生巨大影响。该项目还对少数民族和代表性不足的学生产生了重大的教育影响。