RI: Small: Swarms That 'Hear The Shape of a Drum'

RI：小型：“听到鼓声”的蜂群

• 批准号: 0913015
• 负责人: Herbert Tanner
• 金额: $ 30.56万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0913015&HistoricalAwards=false
• 关键词: RI; Small; Swarms; Hear; Shape

This proposal identifies a pathway to distributed pattern recognition through parallelization of a particular contour identification algorithm and decentralized data collection using formations of robots. Such a system recognizes patterns in the data it collects autonomously. Anticipated applications range from homeland security, to emergency response, scientific exploration and environmental monitoring. Traditional mobile sensor networks are based on an architecture in which some minimal signal processing is performed on the sensing nodes, while the bulk of information is directed to a network sink for processing and interpretation. The hypothesis here is that the same communication infrastructure that enables motion coordination in formation of robots can be exploited for distributed processing of sensor data and autonomous pattern recognition without human intervention. Thus, information is interpreted in a distributed fashion and without dependence on the capabilities of specialized individual nodes. This method brings forward a robust and autonomous system which can inherently tolerate node and network failures and exhibits collective intelligence in the form of group associative memory. Technical challenges to be overcome are the development of decentralized and provably convergent cooperative motion control designs which can enable targeted data collection, and the scalable implementation of a pattern recognition algorithm based on Dirichle Laplacians along with its integration with spatially distributed Hopfield neural networks. The complete system will be demonstrated by an experimental test-bed with mobile robots capable of recognizing noisy, variable shapes on the laboratory floor. Outreach activities will include undergraduate research and summer programs for secondary school teachers.

该提案通过使用机器人的形成来平行分布式识别算法和分散数据收集来标识分布式模式识别的途径。 这样的系统识别其自主收集的数据中的模式。 预期的申请范围从国土安全性到紧急响应，科学探索和环境监测。 传统的移动传感器网络基于一个体系结构，在该体系结构中，在传感节点上执行一些最小信号处理，而大量信息则针对网络接收器进行处理和解释。 这里的假设是，可以利用在机器人形成中实现运动协调的相同通信基础架构，以用于传感器数据的分布式处理和自主模式识别而无需人工干预。 因此，信息以分布式方式解释，并且不依赖专业单个节点的功能。 这种方法带来了一个强大而自主的系统，该系统可以固有地容忍节点和网络失败，并以组关联内存的形式展示集体智能。 要克服的技术挑战是分散且可证明的合作运动控制设计的开发，可以实现目标数据收集，以及基于Dirichle laplacians的模式识别算法的可扩展实施，以及与空间分布的Hopfield Hopfield Neural Networnts的集成。 完整的系统将通过一个实验测试床证明，其移动机器人能够识别实验室地板上嘈杂的可变形状。 外展活动将包括针对中学教师的本科研究和夏季课程。