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).

机器人传感器网络在当前的应用中产生巨大影响。例子包括一个协调的水下滑翔机网络，该网络跟踪化学污染物的运动，监视繁忙的街道的相机网络或提供力保护的一组自动驾驶汽车。在最基本的层面上，这些网络的协调策略涉及代理反复进行测量，与其他代理进行沟通，处理收集的数据并采取措施。这些应用程序中的一个共同假设是向代理商的其他代理和环境状态以及这些策略的同步执行的信息的持续或周期性可用性。这种同步假设在实践中构成了非平凡的挑战，并导致在处理器使用，通信带宽和能源方面的实施效率低下。例如，定期沟通可能会导致浪费使用可用资源。当考虑异步性时，通常以“后验”方式进行：只有在代理时间安排满足条件以确保信息新鲜度的情况下，保证才能保证。尽管从分析的角度来看，这些结果是有效的，但从设计的角度来看，它们并不令人满意，因为确保算法合成中没有内置此类条件。该提案的目的是设计自触发的协调策略的设计，这些策略解释了其他代理商和环境状态的不确定性，并且能够在网络运营中产生大量的能源。关键的概念新颖性是研究整个网络任务的性能如何受代理可用信息的质量影响。这种理解会导致工具并触发各个代理的标准，使他们可以自主决定何时需要新的信息来成功执行所需的任务。自我触发的策略消除了对控制设计阶段的不确定性的持续沟通，感应和重新分布的需求，无缝处理计划的异步执行，并提高代理自主权和网络效率。智能优点：此建议旨在帮助开发工具，摘要，以及自动触发性策略的技术。我们的最终目标是综合处理不确定性和异步性的强大而有效的合作策略，并确保机器人网络以保证的服务质量执行指定的任务，同时以有限的能源供应，带宽和计算资源运行。研究计划沿以下推力结构：（i）可靠的模型和抽象的综合，这些模型和抽象捕获了有关网络和环境状态的不确定性； （ii）识别触发标准，该标准允许代理商确定其当前信息对网络性能的影响的影响； （iii）适用于分析自触发协调策略的稳定性和正确性工具的开发以及其鲁棒性和效率特性的精确表征。我们设想提出的范式将在各种新型协调算法的分布式场景中导致合成，以优化绩效和实施成本之间的权衡，并且具有优越的鲁棒性保证。与现有策略相比：多方面的系统正在扩展人类能力的范围在越来越多的环境中，包括灾难的灾难，包括大洋及其灾难的范围。该项目的结果将有助于设计强大而有效的合作策略，这些策略自然说明了不确定性，并能够在网络运营中节省大量能源。教育活动被整合到研究计划中，包括（i）通过暑期实习，独立学习课程和高级设计项目参与研究的本科研究。 PI属于UCSD Cymer控制系统和动力学中心，并将参与对控制中的行业赞助的本科研究的监督； （ii）提供有关合作控制和研究生监督的研究生课程； （iii）通过加利福尼亚州立大学的数学和科学课程和与UCSD的NSFFUND Project Project Compass合作，针对高中生和教师的外展针对高中生和教师； （iv）通过参加UCSD工程思想学生中心的活动来参与少数族裔学生； （v）广泛的传播活动（期刊出版物，会议，研讨会演示和会议组织）。