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、传感器融合和信息论的概念被结合到一个包含通信和感知目标的单一框架中。这一新的框架通过推理传感器不确定性和无线网络通信对基于模型的主动感知的组合影响，使机器人团队能够实现稳健的智能。通信模型超越了视距或邻近限制，考虑了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。感知策略将在有限的规划范围内执行分布式决策，并将整合新的方法，以快速达到预期的不确定性水平。研究项目包括：i.)机器人传感器网络规划与控制的信息论表述；2.将预定义策略与随机搜索技术相结合的分布式优化；3.)使用滚动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具有鲁棒性；以及i.)实验验证。教育活动将通过关注与开发机器人传感器网络相关的工程挑战、科学问题和社会效益来解决工程和技术管道中的学生泄漏问题。

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.