Self-triggered coordination of robotic networks

机器人网络的自触发协调

• 批准号: 1307176
• 负责人: Jorge Cortes
• 金额: $ 29.07万
• 依托单位: University of California-San Diego
• 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: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1307176&HistoricalAwards=false
• 关键词: Self; triggered; coordination; robotic; networks

There are a myriad of current applications in which robotic sensor networks are having an enormous impact. Examples include a network of coordinated underwater gliders tracking the motion of chemical pollutants, a camera network monitoring a busy street, or a group of autonomous vehicles providing force protection. At the most basic level, coordination strategies for these networks involve agents repeatedly taking measurements, communicating with other agents, processing the collected data, and taking actions in response. A common assumption throughout these applications is the continuous or periodic availability of information to the agents about the state of other agents and the environment, and the synchronous execution of these strategies. This synchronization assumption poses nontrivial challenges in practice and leads to inefficient implementations in terms of processor usage, communication bandwidth, and energy. Periodic communication, for instance, may lead to a wasteful use of the available resources. When asynchronism is considered, it is often done in "a posteriori" fashion: guarantees only hold if the agents time schedules satisfy conditions ensuring the freshness of information. While such results are valid from an analysis viewpoint, they are unsatisfactory from a design perspective because ensuring that such conditions hold is not built into the algorithm synthesis.The objective of this proposal is the design of self-triggered coordination strategies that account for uncertainty in the state of other agents and the environment, and are able to produce substantial energy savings in the network operation. The key conceptual novelty is the study of how the performance of the overall network task is affected by the quality of the information available to the agents. This understanding leads to tools and triggering criteria for individual agents that allow them to autonomously decide when they need fresh information to successfully perform the required task. Self-triggered strategies eliminate the need for continuous communication, sensing, and re-planning, incorporate uncertainty at the control design stage, seamlessly handle asynchronous executions of plans, and increase agent autonomy and network efficiency.Intellectual Merit: This proposal seeks to develop tools, abstractions, and techniques that help design self-triggered cooperative strategies for autonomous robotic sensor networks. Our ultimate goal is to synthesize robust and efficient cooperative strategies that handle uncertainty and asynchronism, and ensure that the robotic network performs the assigned task with guaranteed quality of service, while operating with limited energy supplies, bandwidth, and computational resources. The research plan is structured along the following thrusts: (i) the synthesis of reliable models and abstractions that capture the uncertainty about the state of the network and the environment; (ii) the identification of triggering criteria that allow agents to determine the impact that the actions planned with their current information have on the performance of the network; (iii) the development of stability and correctness tools suited for the analysis of self-triggered coordination strategies and the precise characterization of their robustness and efficiency properties. We envision that the proposed paradigm will lead to the synthesis in a variety of distributed scenarios of novel coordination algorithms that optimize the trade-offs between performance and implementation cost and have superior robustness guarantees than existing strategies.Broader Impacts: Multi-agent systems are extending the range of human capabilities in an increasing number of scenarios, including the study of oceans, disaster recovery, environmental monitoring, and surveillance. The results of this project will help design robust and efficient cooperative strategies that naturally account for uncertainty and are able to produce substantial energy savings in the network operation. The educational activities are integrated into the research plan and consist of (i) undergraduate student involvement in research via summer internships, independent study courses, and senior-design projects. The PI belongs to the UCSD Cymer Center for Control Systems and Dynamics and will be involved in the supervision of industry-sponsored undergraduate research in control; (ii) offering of a graduate course on cooperative control and graduate student supervision; (iii) outreach targeted at high school students and teachers through the California State Summer School for Mathematics and Science program and collaboration with the NSFfunded project ComPASS at UCSD; (iv) involvement of minority students through participation in the activities of the UCSD School of Engineering IDEA Student Center; (v) broad dissemination activities (journal publications, conference, workshop presentations, and conference organization).

目前有无数的应用，其中机器人传感器网络正在产生巨大的影响。例如，一个协调的水下滑翔机网络跟踪化学污染物的运动，一个摄像头网络监控繁忙的街道，或者一组自动驾驶车辆提供武力保护。在最基本的层面上，这些网络的协调策略包括代理反复进行测量、与其他代理通信、处理收集的数据以及采取响应行动。贯穿这些应用程序的一个常见假设是，代理可以连续或定期获得有关其他代理和环境状态的信息，并同步执行这些策略。这种同步假设在实践中带来了不小的挑战，并导致在处理器使用、通信带宽和能量方面的低效率实现。例如，定期通信可能导致对可用资源的浪费。当考虑异步时，通常以“后推”的方式进行：只有当代理时间安排满足确保信息新鲜的条件时，保证才成立。虽然从分析的角度来看，这样的结果是有效的，但从设计的角度来看，它们是不令人满意的，因为确保这些条件成立并没有内置到算法合成中。本提案的目标是设计自触发协调策略，该策略考虑了其他代理状态和环境的不确定性，并且能够在网络运行中产生大量的能源节约。关键的概念新颖性是研究整体网络任务的性能如何受到代理可用信息质量的影响。这种理解导致了单个代理的工具和触发标准，这些工具和触发标准允许它们自主地决定何时需要新的信息来成功地执行所需的任务。自触发策略消除了对持续通信、感知和重新规划的需要，在控制设计阶段纳入了不确定性，无缝地处理计划的异步执行，并提高了代理的自主性和网络效率。智力优势：本提案旨在开发工具、抽象和技术，帮助设计自主机器人传感器网络的自触发协作策略。我们的最终目标是综合鲁棒和高效的协作策略，以处理不确定性和异步性，并确保机器人网络在有限的能源供应、带宽和计算资源的情况下以保证服务质量的方式执行分配的任务。研究计划是按照以下重点构建的：(i)综合可靠的模型和抽象，捕捉网络和环境状态的不确定性；（ii）确定触发标准，使代理能够确定根据其当前信息计划的操作对网络性能的影响；（iii）开发适合分析自触发协调策略的稳定性和正确性工具，并精确表征其稳健性和效率特性。我们设想，所提出的范式将导致各种新型协调算法的分布式场景的综合，这些算法优化了性能和实现成本之间的权衡，并具有比现有策略更好的鲁棒性保证。更广泛的影响：多智能体系统在越来越多的场景中扩展了人类能力的范围，包括海洋研究、灾难恢复、环境监测和监视。该项目的结果将有助于设计稳健高效的合作策略，自然地考虑不确定性，并能够在网络运行中节省大量能源。教育活动被整合到研究计划中，包括(i)本科生通过暑期实习、独立学习课程和高级设计项目参与研究。PI隶属于UCSD Cymer控制系统和动力学中心，将参与监督行业资助的本科生控制研究；（ii）开设关于合作控制和研究生监督的研究生课程；（iii）通过加州州立数学与科学暑期学校项目以及与加州大学圣地亚哥分校国家科学基金会资助的ComPASS项目合作，面向高中学生和教师开展推广活动；（iv）少数民族学生参与加州大学圣地亚哥分校工程学院IDEA学生中心的活动；广泛的传播活动（期刊出版、会议、讲习班发言和会议组织）。