Collaborative Research: An Adaptive Dynamic Programming Approach to the Coordination of Heterogeneous Robotic Sensors Networks

协作研究：协调异构机器人传感器网络的自适应动态规划方法

• 批准号: 1028506
• 负责人: Silvia Ferrari
• 金额: $ 27.44万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028506&HistoricalAwards=false
• 关键词: Collaborative; Research; Adaptive; Dynamic; Programming

The objective of this research is to develop adaptive coordination and control methods for autonomous sensor networks employed for search and rescue operations, humanitarian demining, and ambient monitoring. The approach is to develop adaptive dynamic programming algorithms for this new class of hybrid systems, to optimize their performance over time by coordinating and implementing future sensor actions intelligently, based on prior knowledge and information obtained by the sensors online.Intellectual meritThis research develops novel hybrid adaptive dynamic programming theory and algorithms for robotic sensor networks. Online learning and adaptive control are crucial to robotic sensor networks because they are, by necessity, deployed in highly unstructured and uncertain environments, with little or no prior information about the obstacles or the targets. This research will merge recent interdisciplinary developments in computer science, robotics, and engineering to develop a novel system-theoretic approach that integrates hybrid control with computational geometry algorithms, and optimizes objective functions derived using information and probability theories.Broader impactBy optimizing the performance of emerging sensor technologies and increasing their level of autonomy, this research will enable them to remove or assist humans in carrying out dangerous yet vital missions, such as humanitarian demining, and rescue efforts following hurricanes, fires, or avalanches. Research results will be disseminated and demonstrated on real systems through existing collaborations with the industry and international institutions. Students will be recruited from underrepresented minorities at both institutions, Duke and UNM, to participate in stimulating education activities, including robotic games, computer game competitions, and environmental research.

本研究的目的是开发自适应的协调和控制方法，用于搜索和救援行动，人道主义排雷和环境监测的自主传感器网络。 该方法是开发自适应动态规划算法，这一类新的混合动力系统，以优化其性能随着时间的推移，协调和实施未来的传感器行动智能，先验知识和信息的基础上获得的传感器online.Intellectual meritedThis研究开发新的混合自适应动态规划理论和算法的机器人传感器网络。 在线学习和自适应控制对于机器人传感器网络至关重要，因为它们必须部署在高度非结构化和不确定的环境中，很少或没有关于障碍物或目标的先验信息。 该研究将融合计算机科学、机器人技术和工程学的最新跨学科发展，开发一种新的系统理论方法，将混合控制与计算几何算法相结合，并优化使用信息和概率理论导出的目标函数。更广泛的影响通过优化新兴传感器技术的性能并提高其自主水平，这项研究将使它们能够帮助人类执行危险但至关重要的任务，如人道主义排雷和飓风、火灾或雪崩后的救援工作。 将通过与业界和国际机构的现有合作，在真实的系统上传播和展示研究成果。 学生将从代表性不足的少数民族在这两个机构，杜克和UNM招募，参加刺激的教育活动，包括机器人游戏，电脑游戏比赛，和环境研究。