Tensegrity Models and Shape Control of Vehicle Formations

车辆编队的张拉整体模型和形状控制普林斯顿大学机械和航空航天工程的Naomi Ehrich Leonard这个项目解决了使用反馈来控制车辆编队的形状和几何并实现车辆组的高性能使用方面的关键、公开的问题。最强烈的动机来自使用配备传感器的车辆作为移动传感器网络。在这种情况下，群体形状起着关键作用；例如，它应该被调整，以最大限度地减少对采样场中梯度或边界的估计误差。该项目的主要目标是开发一种方法，使利用从张拉整体结构模型衍生的互联对车辆编队形状的控制进行系统化的设计和分析。移动的多智能体系统可能具有复杂的、多尺度的动力学；为这类系统开发系统和可扩展的控制设计的需要需要超越现有理论和算法的新方法。张拉整体结构是由杆、索、杆相互连接而成的空间网架，具有显著的稳定性和刚性。提出的方法将推导出车辆控制律，模拟张拉整体模型的内力，以产生表现为张拉整体结构的车辆空间网络，特别是继承相同的稳定性特性。力学中的工具可以用来研究受控车辆编队，因为受控动力学是机械系统的动力学。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，将通过对现有张拉整体模型的修改，推导出一种方法来定义和证明具有该形状的张拉整体的动力稳定性。下一步，将扩展该方法，使用由张拉整体结构组成的路径，平滑地控制从一个形状到另一个形状的形成。形状变化将与运动控制算法集成在一起。轨道跟踪的低级控制器将被认为是刚体动力学的协调和稳定控制。配备了测量环境的传感器的车辆组具有巨大的潜力，将彻底改变在空中、陆地和水中进行监测、估计、检测和学习的方式。通过精心设计和协调组内传感车辆的运动，可以最大限度地丰富测量数据的信息，并对手头的问题产生最大的影响。监测森林的火灾和农田的空气破坏，跟踪海洋中的浮游植物水华或濒危的鲸鱼豆荚只是一些例子。PI领导着一个由海洋学家和工程师组成的团队，努力开发一个可持续的海洋观测和预测系统，使用一个配备传感器的自主水下机器人的协调网络。在这种和其他应用中，这项技术已经开始有助于改善对生态系统和全球气候的了解，预测沿海环境的安全条件以部署救援船，改进检测和跟踪化学羽流、泄漏和赤潮的方法，新的搜索和救援手段等等。控制和适应移动车辆编队的形状、几何和图案对于优化监控、估计、检测和学习的性能起着至关重要的作用。该项目专注于设计系统和可靠的算法，用于控制和适应车辆集合的形状、几何和图案。这项研究承诺对从安全到环境的一系列涉及国家利益的问题产生重大影响。

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.