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众多应用中产生了巨大的影响。例如，跟踪化学污染物运动的协调水下滑翔机网络、监控繁忙街道的摄像头网络或提供部队保护的自动驾驶车辆组。在最基本的层面上，这些网络的协调策略涉及代理重复进行测量、与其他代理通信、处理收集的数据并采取响应行动。这些应用程序中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，以及这些策略的同步执行。这种同步假设在实践中提出了不小的挑战，并导致处理器使用、通信带宽和能源方面的低效实现。例如，定期通信可能会导致可用资源的浪费。当考虑异步性时，通常以“后验”方式完成：仅当代​​理时间表满足确保信息新鲜度的条件时才保证保持。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并不令人满意，因为确保这些条件成立并没有内置到算法综合中。该提案的目标是设计自触发的协调策略，以解决以下问题的不确定性：其他代理和环境的状态，并且能够在网络运行中产生大量的能源节省。关键的概念新颖性是研究整个网络任务的性能如何受到代理可用信息质量的影响。这种理解为个体代理带来了工具和触发标准，使他们能够自主决定何时需要新信息来成功执行所需的任务。自触发策略消除了持续通信、感知和重新规划的需要，在控制设计阶段纳入不确定性，无缝处理计划的异步执行，并提高代理自主权和网络效率。智力优点：该提案旨在开发工具帮助设计自主机器人传感器网络的自触发合作策略的抽象和技术。我们的最终目标是综合强大而高效的合作策略来处理不确定性和异步性，并确保机器人网络以有保证的服务质量执行指定的任务，同时在有限的能源供应、带宽和计算资源下运行。该研究计划的结构遵循以下主旨：（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，使代理能够确定利用其当前信息计划的行动对网络性能的影响； (iii) 开发适合于分析自触发协调策略及其鲁棒性和效率特性的精确表征的稳定性和正确性工具。我们设想所提出的范式将导致在各种分布式场景中合成新颖的协调算法，这些算法优化了性能和实施成本之间的权衡，并且比现有策略具有卓越的鲁棒性保证。 更广泛的影响：多智能体系统正在扩展人类在越来越多的场景中的能力范围，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且高效的合作策略，这些策略自然地考虑了不确定性，并且能够在网络运行中产生大量的节能效果。教育活动纳入研究计划，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隶属于加州大学圣地亚哥分校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) 通过加州数学和科学暑期学校计划以及与加州大学圣地亚哥分校 NSF 资助的项目 ComPASS 合作，针对高中生和教师进行宣传； (iv) 通过参与 UCSD 工程学院 IDEA 学生中心的活动来吸引少数族裔学生的参与； (v) 广泛的传播活动（期刊出版物、会议、讲习班演讲和会议组织）。