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

该建议确定了一个分布式模式识别的路径，通过并行化的一个特定的轮廓识别算法和分散的数据收集使用机器人的编队。 这样的系统可以识别其自主收集的数据中的模式。 预期的应用范围从国土安全到应急响应，科学探索和环境监测。 传统的移动的传感器网络是基于这样一种体系结构，在该体系结构中，在感测节点上执行一些最小的信号处理，而大部分信息被引导到网络接收器进行处理和解释。 这里的假设是，相同的通信基础设施，使运动协调形成的机器人可以利用分布式处理的传感器数据和自主模式识别，而无需人为干预。 因此，信息以分布式方式进行解释，而不依赖于专门的各个节点的能力。 该方法提出了一个鲁棒的、自治的系统，该系统能够内在地容忍节点和网络故障，并以组联想记忆的形式表现出集体智能。 要克服的技术挑战是分散的和可证明收敛的合作运动控制设计，可以有针对性的数据收集的发展，以及可扩展的实施的模式识别算法的基础上Dirichle拉普拉斯沿着与空间分布的Hopfield神经网络的集成。 完整的系统将通过一个实验测试台进行演示，该测试台配备了能够识别实验室地板上嘈杂的可变形状的移动的机器人。 外联活动将包括大学生研究和中学教师暑期方案。

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.