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创造了对新的传感器/数据融合和智能控制技术的持续需求。在快速增长的应用程序中，需要从多种来源的有效信息集成方法。在过去的十年中，关于机器人技术的广泛研究表明，使用高级融合算法的测量和数据整合导致自主系统性能的显着改善。特别是，无人驾驶和地面车辆的指导，导航和控制起着关键作用。这对于灵活的操纵器也很重要。该建议基于申请人在传感器信息融合方法的早期工作及其在导航和控制动态系统中的应用。拟议的研究旨在整合大量数据和测量，以允许有效的定位，定位和映射。传感器和数据融合算法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进行研究和开发。主要工作将致力于基于扩展，合奏和自适应Kalman滤波器的高级方法的开发。这些技术将允许融合具有彩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，惯性导航系统和映射的融合。