Tensegrity Models and Shape Control of Vehicle Formations

车辆编队的张拉整体模型和形状控制

• 批准号: 0625259
• 负责人: Naomi Leonard
• 金额: $ 25.2万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625259&HistoricalAwards=false
• 关键词: Tensegrity; Models; Shape; Control; Vehicle

Tensegrity Models and Shape Control of Vehicle FormationsNaomi Ehrich Leonard, Mechanical and Aerospace Engineering, Princeton UniversityThis project addresses critical, open problems in using feedback to control the shape and geometry of a vehicle formation and enable high performance use of vehicle groups. The strongest motivation comes from using sensor-equipped vehicles as a mobile sensor network. In this context group shape plays a critical role; e.g., it should be adapted to minimize error in estimates of gradients or boundaries in a sampled field. The main objective of this project is to develop a methodology that systematizes the design and analysis of control over the shape of a vehicle formation using interconnection that derives from models of tensegrity structures. Mobile, multi-agent systems can have complex, multi-scale dynamics; the need to develop systematic and scalable control design for such systems requires new approaches that go beyond existing theory and algorithms. Tensegrity structures are spatial networks of interconnected struts, cables and rods that have remarkable stabililty and rigidity properties. The proposed approach will derive vehicle control laws that mimic forces internal to tensegrity models to produce spatial networks of vehicles that behave like tensegrity structures and, in particular, inherit the same stability properties. Tools from mechanics can be applied to study the controlled vehicle formation, since the controlled dynamics are those of a mechanical system. The first goal is to solve the "reverse engineering" problem: given an arbitrary shape in 2D or 3D, a method will be derived, using a modification of existing tensegrity models, to define and prove dynamic stability of a tensegrity with this shape. Next, the method will be extended to smoothly control the formation from one shape to another, using a path that consists of tensegrity structures. Shape changes will be integrated with motion control algorithms. Low-level controllers for trajectory tracking will be considered as will coordination and stable control of rigid body dynamics.Groups of vehicles, equipped with sensors to measure the environment, have enormous potential to revolutionize the way that monitoring, estimation, detection and learning can be performed in the air, on land and in the water. With well choreographed and coordinated motion of the sensing vehicles in the group, the measured data can be made maximally information rich and can have the greatest impact on the issue at hand. Monitoring forests for fire and croplands for damage from the air, tracking phytoplankton blooms or endangered whale pods in the ocean are just some examples. The PI leads a team of oceanographers and engineers in an effort to develop a sustainable ocean observing and prediction system using a coordinated network of sensor-equipped, autonomous underwater vehicles. In this and other applications, the technology has already begun to contribute to improved understanding of ecosystems and the global climate, prediction of safe conditions in coastal environments to deploy relief boats, improved methods for detecting and tracking chemical plumes, spills and red tides, new means for search and rescue and more. Control and adaptation of the shape, geometry and pattern of the moving vehicle formation play a critical role in optimizing performance in monitoring, estimation, detection and learning. This project focuses on design of systematic and reliable algorithms for control and adaptation of the shape, geometry and pattern of a vehicle collective. The research promises significant impact on a wide range of issues of national interest from security to the environment.

张拉整体模型和形状控制的车辆编队Naomi Ehrich伦纳德，机械和航空航天工程，普林斯顿大学这个项目解决关键的，开放的问题，在使用反馈控制的形状和几何形状的车辆编队，使高性能的使用车辆组。 最强烈的动机来自于将装有传感器的车辆用作移动的传感器网络。 在这种情况下，群体形状起着关键作用;例如，它应当适于最小化采样场中的梯度或边界的估计中的误差。 该项目的主要目标是开发一种方法，系统化的设计和分析控制的形状的车辆编队使用互联，来自模型的张拉整体结构。 移动的，多智能体系统可以有复杂的，多尺度的动态，需要开发系统和可扩展的控制设计，这样的系统需要新的方法，超越现有的理论和算法。 张拉整体结构是由杆、索、杆相互连接而成的空间网络结构，具有良好的稳定性和刚度。 所提出的方法将推导出车辆控制律，模拟内部的张力整体模型，以产生空间网络的车辆的行为像张拉整体结构，特别是继承相同的稳定性。 由于受控动力学是一个机械系统的动力学，因此可以应用力学的工具来研究受控车辆的形成。 第一个目标是解决“逆向工程”问题：给定2D或3D中的任意形状，将使用现有张拉整体模型的修改来导出一种方法，以定义和证明具有此形状的张拉整体的动态稳定性。 接下来，该方法将被扩展到使用由张拉整体结构组成的路径来平滑地控制从一种形状到另一种形状的地层。 形状变化将与运动控制算法相结合。 轨迹跟踪的低层控制器将被视为刚体动力学的协调和稳定控制。配备传感器测量环境的车辆组具有巨大的潜力，可以彻底改变在空中，陆地和水中进行监测，估计，检测和学习的方式。通过组中传感车辆的精心设计和协调运动，测量数据可以最大限度地丰富信息，并对手头的问题产生最大的影响。 监测森林火灾和农田的空气损害，跟踪浮游植物水华或海洋中濒临灭绝的鲸鱼群只是其中的一些例子。 PI领导一个海洋学家和工程师团队，努力开发一个可持续的海洋观测和预测系统，使用配备传感器的自主水下航行器协调网络。 在这方面和其他方面的应用中，该技术已开始有助于增进对生态系统和全球气候的了解，预测沿海环境的安全状况以部署救援船，改进探测和跟踪化学羽流、溢漏和赤潮的方法，提供新的搜索和救援手段等等。 运动车辆编队的形状、几何形状和模式的控制和适应在优化监测、估计、检测和学习的性能方面起着关键作用。 该项目的重点是设计系统和可靠的算法，用于控制和适应车辆集体的形状，几何形状和模式。 这项研究有望对从安全到环境的一系列国家利益问题产生重大影响。