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

该建议通过并行化特定的轮廓识别算法和使用机器人编队的分散数据收集来确定分布式模式识别的途径。这样的系统可以自动识别它收集的数据中的模式。预计应用范围从国土安全到应急响应、科学探索和环境监测。传统的移动传感器网络是基于一种架构，在这种架构中，一些最小的信号处理在传感节点上执行，而大部分信息被定向到网络接收器进行处理和解释。这里的假设是，使机器人运动协调的通信基础设施可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，以分布式方式解释信息，而不依赖于专门的单个节点的功能。该方法提出了一种鲁棒性自治系统，该系统具有固有的容忍节点和网络故障的能力，并以群体联想记忆的形式表现出集体智能。需要克服的技术挑战是开发分散且可证明收敛的协同运动控制设计，可以实现目标数据收集，以及基于Dirichle laplacian的模式识别算法的可扩展实现，以及它与空间分布式Hopfield神经网络的集成。完整的系统将通过一个实验试验台进行演示，该试验台配备了能够识别实验室地板上嘈杂、可变形状的移动机器人。拓展活动将包括本科生研究和中学教师暑期项目。

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.