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: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=510055
• 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进步对新的传感器/数据融合和智能控制技术产生了持续的需求。数量快速增长的应用程序需要有效的方法来集成多个来源的信息。过去十年对机器人技术的广泛研究表明，使用先进的融合算法集成测量和数据可以显着提高自主系统的性能。特别是无人机和地面车辆的制导、导航和控制发挥着关键作用。这对于灵活的机械手也很重要。该提案建立在申请人早期关于传感器信息融合方法及其在动态系统的导航和控制中的应用的工作的基础上。所提出的研究旨在整合大量数据和测量结果，以实现高效的定位、定位和绘图。将使用传感器数据关联技术、统计方法、粒子过滤和估计技术来研究和开发传感器和数据融合算法。主要工作将致力于开发基于扩展、集成和自适应卡尔曼滤波器的先进方法。这些技术将允许融合具有有色噪声的非线性系统的大量数据，例如图像与全球定位系统、惯性导航系统和地图的融合。