Intelligent guidance, navigation and control in autonomous and aerospace robotic systems

自主和航空航天机器人系统中的智能引导、导航和控制

• 批准号: 5541-2011
• 负责人: Sasiadek, Jurek
• 金额: $ 1.46万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=544266
• 关键词: Intelligent; guidance; navigation; control; autonomous

Rapid advances in autonomous systems technologies create constant demand for new sensor/data fusion and intelligent control techniques. Efficient methods of information integration from several sources are needed in rapidly growing number of applications. Extensive research in robotics in the last decade demonstrated that integration of measurements and data using advanced fusion algorithms lead to significant improvement in performance of autonomous systems. In particular, guidance, navigation, and control of unmanned aerial and ground vehicles play a key role. It is also important for flexible manipulators. This proposal builds on applicant's earlier work on sensor information fusion methods and its application to navigation and control of dynamic systems. The proposed research is aimed at integration of large amount of data and measurements to allow efficient localization, positioning and mapping. Sensor and data fusion algorithms will be investigated and developed using sensor data association techniques, statistical methods, particle filtering and estimation techniques. The main effort will be devoted to the development of advanced methods based on extended, ensemble and adaptive Kalman filter. These techniques would allow fusion of large amount of data for nonlinear systems with coloured noise, e.g. fusion of images with global positioning systems, inertial navigation systems and mapping.

自主系统技术的快速发展对新的传感器/数据融合和智能控制技术产生了持续的需求。在快速增长的应用中，需要来自多个来源的有效的信息集成方法。过去十年在机器人学方面的广泛研究表明，使用先进的融合算法整合测量和数据可以显著提高自主系统的性能。特别是，无人机和地面飞行器的制导、导航和控制起着关键作用。对于柔性机械手来说，这也很重要。这项建议建立在申请人早期在传感器信息融合方法及其在动态系统导航和控制中的应用方面的工作的基础上。拟议的研究旨在整合大量数据和测量，以实现高效的定位、定位和测绘。将使用传感器数据关联技术、统计方法、粒子滤波和估计技术来研究和开发传感器和数据融合算法。主要工作将致力于发展基于扩展、集成和自适应卡尔曼滤波的先进方法。这些技术将允许对有色噪声的非线性系统进行大量数据融合，例如图像与全球定位系统、惯性导航系统和测绘的融合。