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

• 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

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。这种系统的先进控制将越来越多地受益于廉价的固态摄像机，但由此产生的基于视觉的系统将是计算密集型的，并引入显著的延迟。众所周知，延迟会导致控制系统的不稳定，可能导致关键基础设施的灾难性故障。该项目将开发补偿和克服网络控制系统中这种延迟的方法。该项目还将促进科学和技术人力资源的开发，继续研究人员的努力，他们在过去的项目中监督了来自代表性不足群体的24名学生，包括妇女，西班牙裔和非洲裔美国人。在该项目中开发的方法是专为有效利用图像数据，以促进实时多智能体路径规划和避免碰撞。这将通过结合使用几何表示和基于特征的学习来实现。方位和3-D位置将被表征为HSV颜色分类的对象被抓住，障碍物，以及对象被放置的目的地。这些方向和三维位置将用于实时生成无碰撞路径，以用作理想的轨迹。将制定计算效率高的完全分散和节能的极值寻求控制方案，利用固有的并行和高度冗余的处理架构，并在存在相互关联的非线性，饱和和不确定性的情况下运行。实验验证的结果，使用28阶巴克斯特机器人，将进行检查的鲁棒性和验证的计算能源效率的algorithm.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

• DOI: 10.1115/dscc2019-8951
• 发表时间: 2019-10
• 期刊: Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain
• 作者: Alex Bertino;M. Bagheri;M. Krstić;Peiman Naseradinmousavi

Adaptive control of a two-link robot using batch least-square identifier

• DOI: 10.1109/jas.2020.1003459
• 发表时间: 2021-01
• 期刊: IEEE/CAA Journal of Automatica Sinica
• 作者: M. Bagheri;I. Karafyllis;Peiman Naseradinmousavi;M. Krstić

Feedback linearization based predictor for time delay control of a high-DOF robot manipulator

• DOI: 10.1016/j.automatica.2019.06.037
• 发表时间: 2019-10-01
• 期刊: AUTOMATICA
• 影响因子: 6.4
• 作者: Bagheri, Mostafa;Naseradinmousavi, Peiman;Krstic, Miroslav
• 通讯作者: Krstic, Miroslav

Experimental and Analytical Delay-Adaptive Control of a 7-DOF Robot Manipulator

七自由度机器人机械臂的实验和分析延迟自适应控制

• DOI: 10.23919/acc50511.2021.9483086
• 发表时间: 2021
• 期刊: American Control Conference
• 作者: Bertino, Alexander;Naseradinmousavi, Peiman;Krstic, Miroslav
• 通讯作者: Krstic, Miroslav