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

机器人、航天工程和医学中自主系统的智能导航、导航和控制系统是本提案的主要重点。不断增加的自主性要求使系统变得越来越复杂，因此更难控制。最近的事件突出了自主空间和空中机器人在各种任务中的效用，包括监视、空间探索等。这些系统的主要问题是其准确定位、定位和导航的能力。空间机器人用于探索外行星，也用于清除较高轨道上的空间碎片。为了实现无人机或空间机器人的自主导航，机器人必须能够估计位置和姿态。机器人的位置和姿态或姿态并不容易确定。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。所有的传感器都存在许多问题和缺点，因此，使用先进的传感器/数据融合算法来估计位置和/或位置。这项建议建立在申请人早先对传感器/数据融合方法以及机器人的智能导航、导航和控制所做的调查的基础上。 拟议研究的目标是使移动的无人驾驶系统完全自主，并开发先进的导航、导航和控制系统，用于自动驾驶车辆和其他基于视觉系统的运动和动力学系统。具体目标包括： 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.研究开发新的先进制导、导航和控制系统及算法，用于无人自主移动车辆和航天机器人以及其他自主系统； 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.研究和开发具有柔性连接和关节的机器人导航和定位的先进控制系统。 所建议的方法将基于自适应和扩展卡尔曼滤波(EKF)以及Unscented卡尔曼滤波(UKF)。应用于卡尔曼滤波增益的自适应过程将基于模糊逻辑方法。这将确保在带有有色噪声的信号的情况下收敛。这一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方法。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。预计在机器人环境感知和控制方面将取得重大进展。

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.