Collaborative Research: Ergodic Trajectories in Discrete Mechanics

协作研究：离散力学中的遍历轨迹

• 批准号: 1334759
• 负责人: Melvin Leok
• 金额: $ 19.49万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334759&HistoricalAwards=false
• 关键词: Collaborative; Research; Ergodic; Trajectories; Discrete

The objective of this research project is to develop efficient and robust numerical methods for searching and exploring an environment using a mechanical system, while ensuring that the average temporal coverage reproduces a prescribed spatial distribution. This is achieved by developing methods for controlling mechanical systems so that they are ergodic with respect to the given spatial distribution, and combining them with geometric structure-preserving numerical integrators which have good backward error properties, and preserve geometric invariants like the symplectic structure, energy, and momentum. Furthermore, these methods preserve the nonlinear structure of the configuration manifold, such as the Lie group or homogeneous space structure. The key technical goals include: (i) the development and analysis of structured integrators to accurately predict ergodic properties of a given system; (ii) the development of simulation-based optimization of system parameters, and controls to maximize efficiency of ergodic search; (iii) the generalization of these techniques to Lie groups and homogeneous spaces, enabling ergodic search for a rich class of robotic systems; (iv) experimental validation on realistic systems.These techniques will directly be applicable to a broad range of real-world industrial applications, including joint space exploration for robotic systems, fault detection in manufacturing, and optimal search, coverage, and information extraction for autonomous sensor networks. This industrial outreach will be facilitated by the release of public-domain software that will lower the barrier to adapting geometric numerical integration techniques in a variety of applications. Furthermore, we will engage in public outreach activities by teaming up with the Museum of Science and Industry in Chicago to develop an interactive exhibit demonstrating search algorithms for mechanical systems. These concrete applications serve to inspire high-school students (in particular, underrepresented minorities and women) to pursue STEM degrees, and this in turn will help to secure the long-term economic innovation and competitiveness of American industry.

该研究项目的目标是开发高效且稳健的数值方法，用于使用机械系统搜索和探索环境，同时确保平均时间覆盖再现规定的空间分布。这是通过开发控制机械系统的方法来实现的，使它们相对于给定的空间分布是遍历的，并将它们与具有良好后向误差特性的几何结构保持数值积分器相结合，并保持辛结构、能量和动量等几何不变量。此外，这些方法保留了构型流形的非线性结构，例如李群或齐次空间结构。关键技术目标包括：（i）开发和分析结构化积分器，以准确预测给定系统的遍历特性； (ii) 开发基于仿真的系统参数优化和控制，以最大限度地提高遍历搜索的效率； (iii) 将这些技术推广到李群和齐次空间，从而能够对丰富的机器人系统进行遍历搜索； (iv) 对现实系统的实验验证。这些技术将直接适用于广泛的现实世界工业应用，包括机器人系统的联合空间探索、制造中的故障检测以及自主传感器网络的最佳搜索、覆盖和信息提取。公共领域软件的发布将促进这种工业推广，这将降低在各种应用中采用几何数值积分技术的障碍。此外，我们将与芝加哥科学与工业博物馆合作开发一个交互式展览，展示机械系统的搜索算法，从而参与公共推广活动。这些具体应用有助于激励高中生（特别是代表性不足的少数族裔和女性）攻读 STEM 学位，这反过来又有助于确保美国工业的长期经济创新和竞争力。