Guidance, Navigation and Control in Robotics, Medical and Aerospace Systems

机器人、医疗和航空航天系统中的制导、导航和控制

• 批准号: RGPIN-2016-06255
• 负责人: Sasiadek, Jurek
• 金额: $ 2.4万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710236
• 关键词: Guidance; Navigation; Control; Robotics; Medical

The intelligent guidance, navigation and control systems for autonomous systems in robotics, aerospace engineering and medicine are the main focus of this proposal. Increasing requirements for autonomy make systems more and more complex, and, therefore more difficult to control. Recent events have highlighted the utility of the autonomous space and aerial robots for various tasks, including surveillance, space exploration, etc. The main problem with those systems is its ability for accurate localization, positioning and navigation. Space robots are used for outer planet exploration but also for space debris removal from higher orbits. In order to navigate autonomously UAVs or space robots must be able to estimate location and attitude. Position and attitude or pose of the robot is not easy to determine. It may involve, depending of the applications, inertial navigation sensors, GPS, and vision systems. All sensors suffer from number of problems and shortcomings and therefore, advanced sensor/data fusion algorithms are used to estimate position and/or location. This proposal builds on earlier investigation done by the applicant on sensor/data fusion methods and intelligent guidance, navigation and control for robotics. The objective of the proposed research is to make mobile, unmanned systems fully autonomous, and to developed advanced guidance, navigation, and control systems for autonomous vehicles and other moving and dynamic systems based on vision systems. The particular objectives are: 1. To investigate and develop new advanced intelligent methods and algorithms for sensor and data fusion that would allow autonomous systems better perception of its environment; 2. To investigate and develop new advanced guidance, navigation and, control systems and algorithms for unmanned autonomous mobile vehicles and aerospace robots, as well as, other autonomous systems; 3. To develop advanced localization and positioning methods for mobile robots and stationary manipulators that could be used for aerospace, medical and energy applications: 4. To investigate and develop advanced control systems for guidance and positioning of robots with flexible links and joints. The suggested methodology will be based on Adaptive and Extended Kalman Filtering (EKF), as well as, Unscented Kalman Filter (UKF). The adaptive procedure applied to the Kalman Filter gain will be based on fuzzy logic methodology. This would ensure the convergence in the case of signals with coloured noise. This procedure will be compared with some chosen statistical and probabilistic method. In particular, an extended Monte Carlo method and Particle Filter methods will be used. The developed methods, algorithms, and procedures will be applied to aerospace robots, mobile robots and medical manipulators. It is expected that significant progress will be made in robots environment perception and control.

机器人、航空航天工程和医学领域自主系统的智能制导、导航和控制系统是该提案的主要焦点。对自主性的要求越来越高，使得系统变得越来越复杂，因此更难以控制。最近的事件凸显了自主空间和空中机器人在监视、太空探索等各种任务中的实用性。这些系统的主要问题是其精确定位、定位和导航的能力。太空机器人用于外行星探索，也用于从更高轨道清除空间碎片。为了自主导航，无人机或太空机器人必须能够估计位置和姿态。机器人的位置和姿态或位姿不容易确定。 根据应用的不同，它可能涉及惯性导航传感器、GPS 和视觉系统。所有传感器都存在许多问题和缺点，因此，先进的传感器/数据融合算法用于估计位置和/或位置。 该提案建立在申请人早期对传感器/数据融合方法以及机器人智能引导、导航和控制进行的调查的基础上。 拟议研究的目标是使移动无人系统完全自主，并为自动驾驶车辆和其他基于视觉系统的移动和动态系统开发先进的制导、导航和控制系统。 具体目标是： 1. 研究和开发用于传感器和数据融合的新的先进智能方法和算法，使自主系统能够更好地感知其环境； 2. 研究和开发新型先进的无人自主移动车辆和航空航天机器人以及其他自主系统的制导、导航和控制系统和算法； 3. 开发可用于航空航天、医疗和能源应用的移动机器人和固定机械手的先进定位和定位方法： 4. 研究和开发先进的柔性连杆和关节机器人引导和定位控制系统。 建议的方法将基于自适应和扩展卡尔曼滤波 (EKF) 以及无迹卡尔曼滤波器 (UKF)。应用于卡尔曼滤波器增益的自适应过程将基于模糊逻辑方法。这将确保信号具有有色噪声的情况下的收敛。该过程将与一些选定的统计和概率方法进行比较。特别是，将使用扩展的蒙特卡罗方法和粒子滤波器方法。所开发的方法、算法和程序将应用于航空航天机器人、移动机器人和医疗机械手。预计机器人环境感知与控制方面将取得重大进展。