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Models and Algorithms for Optimal Vision-Based Surveillance and Exploration of Complex Environments

基于最佳视觉的复杂环境监控和探索的模型和算法

基本信息

批准号：
2110895
负责人：
Yen-Hsi Tsai
金额：
$ 40.96万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110895&HistoricalAwards=false
关键词：
Models Algorithms Optimal Vision Based

项目摘要

The PI plans to develop the mathematics and corresponding algorithms to determine optimal locations to observe and map out complex unknown domains with moving obstacles. This project is motivated by the increasing number of sensor-equipped mobile robotic devices and unmanned vehicles required to perform surveillance missions. In many of these missions, efficiency is essential — maximizing the information gain with minimal measurements by the sensors (observations) reduces data transmission, power consumption and increases robustness and capacity. Such optimization is the principal aim of the research program. This grant will support 1 graduate student per year for the 3 year duration of the grant. The PI will develop iterative greedy algorithms that determine observation locations to optimize each step's information gain. The "gain" is formulated as an integral operator on the domain shape and is costly to compute. The PI proposes developing Deep Learning approaches that make computations feasible, particularly when the domain shapes are at best partially known. The proposed algorithms will require generating training data by offline numerical simulations. This approach is crucial to sample high-dimensional shape space in a manner consistent with the dynamical processes dictated by the governing mathematical algorithms. This project includes three main thrusts: 1) Development of non-myopic greedy algorithms and study their properties and efficiency. 2) Development of Deep Learning models and data generation for learning the gain functions. 3) Development of mathematical understanding of U-net used the Deep Learning models used in the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PI计划开发数学和相应的算法，以确定观察和绘制具有移动障碍物的复杂未知区域的最佳位置。这个项目的动机是越来越多的传感器配备的移动的机器人设备和无人驾驶车辆需要执行监视任务。在许多这些任务中，效率是至关重要的-通过传感器的最小测量（观察）最大限度地提高信息增益，减少数据传输，功耗，并提高鲁棒性和容量。这种优化是研究计划的主要目标。该补助金将支持每年1名研究生，为期3年。PI将开发迭代贪婪算法，确定观察位置，以优化每一步的信息增益。 “增益”被公式化为域形状上的积分算子，并且计算成本高。PI建议开发深度学习方法，使计算变得可行，特别是当域形状充其量只是部分已知时。所提出的算法需要通过离线数值模拟生成训练数据。这种方法是至关重要的采样高维形状空间的方式一致的动态过程所规定的数学算法。本项目主要包括三个方面的工作：1）开发非近视贪婪算法，并研究其性质和效率。2)开发深度学习模型和数据生成，用于学习增益函数。3)该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。