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：
1823983
负责人：
Miroslav Krstic
金额：
$ 17.95万
依托单位：
University of California-San Diego
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 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823983&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.

大型机电试剂的大型网络，包括复杂的机器人操纵器和自动驾驶汽车，很快就会无处不在。这种系统的高级控制将越来越受益于廉价的固态摄像机，但是由此产生的基于视觉的系统将是计算密集型的，并引入了重大延迟。已知延迟会导致控制系统的不稳定，导致关键基础设施的灾难性失败。该项目将开发方法来补偿和克服网络控制系统中的此类延迟。该项目还将为科学技术领域的人力资源发展做出贡献，继续研究人员的努力，这些调查人员监督了来自代表性不足的团体的二十名学生在过去的项目中，包括妇女，西班牙裔和非裔美国人。项目中开发的方法是根据有效利用图像数据来量身定制的，以促进实时多代理路径计划和避免碰撞。这将使用几何表示和基于特征的学习的组合来实现。对于要抓住的HSV颜色分类对象，障碍物以及要放置对象的目的地，方向和3-D位置将被表征。这些方向和3-D位置将用于实时生成无碰撞路径，以用作理想的轨迹。将制定计算有效的完全分散和节能的极值寻求控制方案，利用固有的并行且高度冗余的处理体系结构，并在存在互连的非线性，饱和度和不确定性的情况下运行。使用28阶百特机器人对结果的实验验证将进行检查，以检查稳定性并验证算法的计算能力效率。该奖项反映了NSF的法定任务，并被视为通过使用评估的支持，基金会的智力优点和更广泛的影响审查标准。