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Collaborative Research: Decentralized Adaptive and Extremum Seeking Control of Robot Manipulators Using Image Processing

协作研究：使用图像处理的机器人机械手的分散自适应和极值搜索控制

基本信息

批准号：
1823951
负责人：
Peiman Naseradinmousavi
金额：
$ 23.04万
依托单位：
San Diego State University Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823951&HistoricalAwards=false
关键词：
Collaborative Research Decentralized Adaptive Extremum

项目摘要

Large networks of electromechanical agents, including complex robotic manipulators and self-driving cars, will soon become ubiquitous. Advanced control of such systems will increasingly benefit from inexpensive solid-state cameras but the resulting vision-based systems will be computation-intensive and introduce significant delays. Delays are known to cause instability in control systems, leading potentially to catastrophic failures in critical infrastructure. This project will develop methods to compensate and overcome such delays in networked control systems. The project will also contribute to the development of human resources in science and technology, continuing the efforts of the investigators who have supervised two dozen students from underrepresented groups on past projects, including women, Hispanics, and African-Americans. The methods developed in the project are tailored to the efficient use of image data to facilitate real-time multi-agent path planning and collision avoidance. This will be accomplished using a combination of geometric representations and feature-based learning. Orientations and 3-D positions will be characterized for HSV color-classified objects to be grasped, for obstacles, and for destinations where the objects are to be placed. These orientations and 3-D positions will be employed for real-time generation of collision-free paths to be used as desirable trajectories. Computationally efficient fully decentralized and energy-efficient extremum seeking control schemes will be formulated, leveraging inherently parallel and highly redundant processing architecture and operating in the presence of interconnected nonlinearities, saturations, and uncertainties. Experimental validation of the results, using a 28th-order Baxter robot, will be carried out to examine the robustness and to validate the computational-energy efficiency of the algorithms.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.

大型机电代理网络，包括复杂的机器人操纵器和自动驾驶汽车，将很快变得无处不在。此类系统的高级控制将越来越多地受益于廉价的固态相机，但由此产生的基于视觉的系统将是计算密集型的，并会引入显着的延迟。众所周知，延迟会导致控制系统不稳定，从而可能导致关键基础设施出现灾难性故障。该项目将开发补偿和克服网络控制系统中此类延迟的方法。该项目还将有助于科学技术人力资源的开发，继续研究人员的努力，这些研究人员在过去的项目中监督了来自代表性不足群体的两打学生，包括妇女、西班牙裔和非裔美国人。该项目开发的方法专为图像数据的有效使用而定制，以促进实时多智能体路径规划和避免碰撞。这将通过结合几何表示和基于特征的学习来完成。将针对要抓取的 HSV 颜色分类对象、障碍物以及要放置对象的目的地来表征方向和 3-D 位置。这些方向和 3D 位置将用于实时生成无碰撞路径，用作所需的轨迹。将制定计算高效的完全分散和节能的极值搜索控制方案，利用固有的并行和高度冗余的处理架构，并在存在互连非线性、饱和度和不确定性的情况下运行。将使用 28 阶 Baxter 机器人对结果进行实验验证，以检查算法的稳健性并验证算法的计算能源效率。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。