RI: Information-Theoretic Control of Robotic Sensor Networks

该项目将为机器人传感器网络的规划和控制开发一个信息理论框架。分布式传感器网络，协同控制，网络通信，传感器融合和信息理论的概念被组合成一个单一的框架，结合了通信和传感目标。这种新的框架，使强大的智能机器人团队通过推理的传感器的不确定性和无线网络通信的基于模型的主动传感的综合影响。通信模型超越了视线或接近度的限制，以考虑信道容量对相对分离、异步通信以及无法建模的干扰或噪声的依赖性。传感策略将在有限的规划范围内执行分布式决策，并将集成新方法以快速实现所需的不确定性水平。研究重点包括：i.）机器人传感器网络规划和控制信息理论公式; ii.）分布式优化，其将预定义策略与随机搜索技术相结合; iii.）使用滚动时域控制和多变量极值搜索对传感器不确定性和无线电噪声的鲁棒性;以及iv.）实验验证教育活动将通过关注与机器人传感器网络开发相关的工程挑战、科学问题和社会效益，解决学生从工程和技术管道中流失的问题。

This project will develop an information-theoretic framework for the planning and control of robotic sensor networks. Concepts from distributed sensor networking, cooperative control, networked communication, sensor fusion, and information theory are combined into a single framework that incorporates both communication and sensing objectives. This new framework enables robust intelligence in robot teams through reasoning about the combined effects of sensor uncertainty and wireless networked communication on model-based active sensing. Communication models move beyond line-of-sight or proximity constraints to consider the dependency of channel capacity on relative separation, asynchronous communication, and interference or noise that cannot be modeled. Sensing strategies will perform distributed decision-making over finite planning horizons and will integrate new methods to achieve desired levels of uncertainty rapidly. Research thrusts include: i.) information-theoretic formulation of robot sensor network planning and control; ii.) distributed optimization that combines predefined maneuvers with random search techniques; iii.) robustness to sensor uncertainty and radio noise using receding horizon control and multivariable extremum seeking; and iv.) experimental validation. Education activities will address the leakage of students from the engineering and technology pipeline by focusing on the engineering challenges, scientific questions, and societal benefits associated with the development of robot sensor networks.