CAREER: Efficient Multi-Vehicle Coordination for Distributed Sensing and Estimation

群体协调飞行器的使用正在彻底改变在空中、水下和地面环境中进行观测、测量和估计的方式。特别是，多车辆系统特别适合在偏远地区进行主动监测和协调行动。它们进行现场实时测量的能力，以及动态适应和与环境整合的额外能力，使它们在广泛的场景中优于静态传感器网络。 将单个车辆的运动、传感、通信和计算能力集成到具有可预测行为的复杂而强大的系统中的科学仍处于原始阶段。本计画的目的是开发新的工具来分析分散式系统及其设计的复杂性，著重于分散式估测、能量限制操作、以及车辆动力学等方面，研究计画沿着下列重点：（i）研究物理过程分散式估测的空间内插方法，以及最佳感测的可证明正确的协调演算法的合成;以及（ii）考虑到多车辆系统的非平凡动态的节能协调政策的设计。 创新的技术方法依赖于一组有前途的建模和分析结果，最近由PI和合作者开发，基于几何优化，图论，系统和控制理论。更广泛的影响：这项研究将对多车辆系统在科学领域和救灾行动中的使用产生重大影响。也就是说，在实时遥感信息和数据收集十分重要的情况下。 PI计划与加州大学圣地亚哥分校斯克里普斯海洋研究所合作，为用于气候和大气研究的多架无人驾驶飞行器实施有效的合作算法。该提案的其他教育活动包括：（i）建立一个自主多车辆系统实验室，作为模拟和验证合作算法的工具，并通过设计项目和暑期实习让本科生参与研究。 (ii)在加州数学和科学暑期学校（COSMOS）项目中为有才华的高中生开设一门课程，重点是机器人系统及其协调

The use of groups coordinated vehicles is revolutionizing the way in which observation, measurement, and estimation is carried out in aerial, underwater, and ground environments. In particular, multi-vehicle systems are uniquely suited for active monitoring and coordinated action in remote areas. Their ability to take in-situ, real-time measurements, together with the additional capacity for dynamic adaptation and integration with the environment makes them preferable over static sensor networks in a wide range of scenarios. The science of integrating the motion, sensing, communication, and computation capabilities of individual vehicles into complex and robust systems with predictable behavior is still at a primitive stage. This project aims to develop novel tools to analyze the complexity of distributed systems and their design, focusing on aspects such as distributed estimation, energy-constrained operation, and vehicle dynamics.The research plan is articulated along the following thrusts: (i) the investigation of spatial interpolation methods for decentralize estimation of physical processes and the synthesis of provably correct coordination algorithms for optimal sensing; and (ii) the design of energy-efficient coordination policies that take into account the non-trivial dynamics of multi-vehicle systems. The innovative technical approach relies on a set of promising modeling and analysis results, recently developed by the PI and collaborators, based on geometric optimization, graph theory, and systems and control theory.Broader Impacts: This research will have a significant impact on the use of multi-vehicle systems in scientific domains and disaster relief operations; that is, in those scenarios where real-time remotely sensed information and data gathering are important. In collaboration with the Scripps Institution of Oceanography at UCSD, the PI plans to work on the implementation of efficient cooperative algorithms for multiple unmanned aerial vehicles used in climate and atmospheric research.Additional educational activities of this proposal include: (i) The creation of an Autonomous Multi-Vehicle Systems Laboratory, which will serve as a tool to simulate and validate cooperative algorithms, and as a means to involve undergraduates in research via design projects and summer internships. (ii) The offering of a course for talented high-school students in the California Summer School of Mathematics and Science (COSMOS) program, with a focus on robotic systems and their coordination