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Detection and Tracking of Multiple Dynamic Targets with Cooperating Networked Agents

通过协作网络代理检测和跟踪多个动态目标

基本信息

批准号：
1509084
负责人：
Sean Andersson
金额：
$ 40万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1509084&HistoricalAwards=false
关键词：
Detection Tracking Multiple Dynamic Targets

项目摘要

In the multi-agent framework, a team of autonomous agents cooperates in carrying out complex tasks in an environment that is potentially dynamic, hazardous, and even adversarial. In general, the team must seek out and then monitor targets that may also be moving while balancing the monitoring task with continued exploration. This setting, broadly termed persistent monitoring, typically arises in mobile robotic applications and sensor networks, but it is surprisingly rich and encompasses a number of other, less obvious, domains. In this project we will develop mathematical techniques for the optimal, or at least near-optimal, behavior of a team of autonomous agents performing persistent monitoring and deploy the theory in the context of tracking multiple biological macromolecules moving inside living cells. In additional to foundational mathematical research with a broad scope, the project aims to construct a new tracking fluorescence microscope that will leverage the mathematical framework to provide significantly better speed, accuracy, and throughput than existing instruments for following the dynamics of single molecules. Both undergraduate and graduate students will be trained in a variety of disciplines, including optimization, control theory, robotics, and microscopy. In addition, the project involves outreach to low-income, first-generation-to-college students in the Boston metro area through the development of one-day modules in single molecule imaging that will be used as part of Nanocamp, a six-week residential summer program for rising high school sophomores and juniors in the target demographic.The control and coordination of agents in dynamic, hazardous, and possibly adversarial environments is highly challenging since it involves multiple objectives and a considerable amount of information exchange with often severe communication limitations. Since the use of ad hoc control policies frequently leads to poorly performing systems, the approach proposed in this project is the use of optimization methods to create well-designed, rational policies that can guarantee satisfactory, if not optimal, behavior. Because such optimization problems rapidly get computationally intractable and their solution is rarely amenable to on-line scalable, distributed implementations, one of the specific aims is to develop near-optimal, efficient, and uncertainty-robust schemes that use a parametric family of control policies that can be optimized on-line. While the primary project goal is a mathematically rigorous and broadly applicable framework, it will be developed with the primary motivating application in mind, namely tracking of multiple single biological macromolecules. In this setting, the agents are individual confocal volumes, each independently addressed and controlled using a programmable array microscope, and the targets are fluorescently-labeled biological macromolecules. While a decentralized implementation is in general desirable, the single molecule tracking application supports a centralized solution since all implementation is done on a single controller and thus the project will focus on the centralized approach. The mathematical algorithms developed will be implemented on field programmable gate array (FPGA) devices and tested through experiment by tracking freely diffusing quantum dots.

在多智能体框架中，一组自主智能体在一个潜在的动态、危险甚至敌对的环境中合作执行复杂的任务。一般来说，小组必须寻找并监测可能也在移动的目标，同时平衡监测任务和继续探索。这种设置，广义上称为持续监控，通常出现在移动的机器人应用和传感器网络中，但它是令人惊讶的丰富，并涵盖了许多其他不太明显的领域。在这个项目中，我们将开发数学技术的最佳，或至少接近最佳的，一个团队的自主代理执行持续监测的行为，并部署在跟踪活细胞内移动的多个生物大分子的背景下的理论。除了广泛的基础数学研究外，该项目还旨在构建一种新的跟踪荧光显微镜，该显微镜将利用数学框架提供比现有仪器更好的速度，准确性和通量，以跟踪单分子的动态。本科生和研究生都将接受各种学科的培训，包括优化，控制理论，机器人和显微镜。此外，该项目还涉及通过开发为期一天的单分子成像模块，向波士顿大都会地区的低收入，第一代大学生提供服务，该模块将用作Nanocamp的一部分，Nanocamp是一个为期六周的住宅夏季计划，针对目标人群中正在上升的高中生和大三学生。并且可能是对抗性的环境是非常具有挑战性的，因为它涉及多个目标和相当数量的信息交换，并且通常具有严重的通信限制。由于使用自组织控制策略经常会导致性能不佳的系统，在这个项目中提出的方法是使用优化方法来创建设计良好的，合理的政策，可以保证令人满意的，如果不是最佳的，行为。由于这样的优化问题迅速得到计算上棘手的和他们的解决方案是很少服从在线可扩展的，分布式的实现，具体的目标之一是开发接近最优的，高效的，和不确定性鲁棒的计划，使用一个参数家庭的控制策略，可以在线优化。虽然主要的项目目标是一个数学上严格和广泛适用的框架，它将与主要的激励应用程序，即跟踪多个单一的生物大分子。在这种情况下，代理人是单独的共聚焦体积，每个独立寻址和控制使用可编程阵列显微镜，和目标是荧光标记的生物大分子。虽然分散式实施通常是可取的，但单分子跟踪应用程序支持集中式解决方案，因为所有实施都是在单个控制器上完成的，因此该项目将专注于集中式方法。所开发的数学算法将在现场可编程门阵列（FPGA）设备上实现，并通过跟踪自由扩散的量子点进行实验测试。