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.

机器人网络可以在许多不同的领域产生巨大的影响，如救灾，应急响应和国家安全。最近的灾难，如2012年的飓风桑迪或2011年的日本地震，提醒我们无人驾驶的自主网络作为我们社会的一部分可以发挥至关重要的作用。该项目的目标是引入一种新的多学科设计范式，通过传感，通信和导航的共同优化，成功运行移动的机器人网络。在机器人/控制界，大多数现有的工作不处理现实的通信问题（如阴影和多径衰落）和理想的链路/磁盘模型被假定为预测连接。 另一方面，通信和网络社区通常不关心路径规划和导航。在机器人网络中，路径规划不仅影响感知质量，而且影响连接维护。这种多学科的性质使得设计鲁棒的决策策略，在机器人网络中成功完成任务具有相当大的挑战性和开放性问题。此外，给定的传感、通信和导航资源的单独优化可能不足以在资源约束下成功运行。（运动和通信），时间和带宽资源，并奠定相应的最佳传感，通信和导航协同设计策略的基础，其中包括轨迹，传感，连接性、运动速度/功率和通信传输速率/功率优化。在这种方法中，现实的概率连接性度量与传感和导航目标进行了适当的协同优化，使得每个机器人选择一个轨迹，使其能够最大限度地收集信息，同时保持所需的连接性。这个框架回答了一些基本问题，例如何时投资于运动，何时投资于通信。 该项目还探讨了任务的可行性以及信息生成、收集和交流的基本局限性，这可以为部署前的资源规划提供重要的见解。 总的来说，这种新的协同优化基础使机器人网络能够在有限的资源下成功运行，从而对我们的社会产生巨大的影响。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。