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

机器人网络可以在许多不同的领域产生巨大影响，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，提醒我们无人自主网络作为我们社会的一部分可以发挥关键作用。该项目的目标是通过传感、通信和导航的共同优化，为移动机器人网络的成功运行引入一种新的多学科设计范例。在机器人/控制领域，大多数现有的工作都没有处理现实的通信问题(如阴影和多径衰落)，并且假设理想的链路/磁盘模型来预测连通性。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，而且影响连通性维护。这种多学科的性质使得设计健壮的决策策略以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。此外，对于给定的感知、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。在本研究中，重点关注有限的能量(运动和通信)、时间和带宽资源的影响以及相应的最优感知、通信和导航联合设计策略的基础，其中包括轨迹、感知、连通性、运动速度/功率和通信传输速率/功率优化。在这种方法中，现实的概率连通性度量与传感和导航目标适当地协同优化，以便每个机器人选择一条允许其在保持所需连通性的同时最大化其信息收集的轨迹。这一框架回答了一些基本问题，如何时投资于移动和何时投资于通信。该项目还探讨了任务的可行性和信息生成、收集和交流的基本限制，可为部署前的资源规划提供关键的见解。总体而言，这种新的联合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影响。该项目还对少数群体和代表性不足的学生产生了重大的教育影响。

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.