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

该提案的主要重点是机器人、航空航天工程和医学中自主系统的智能制导、导航和控制系统。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。最近的事件突出了自主空间和航空机器人的各种任务，包括监视，空间探索等，这些系统的主要问题是它的准确定位，定位和导航的能力。空间机器人用于外行星探索，但也用于从较高轨道清除空间碎片。为了自主导航，无人机或空间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势不容易确定。根据应用，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，使用先进的传感器/数据融合算法来估计位置和/或定位。该建议建立在申请人对传感器/数据融合方法和机器人智能制导、导航和控制所做的早期研究的基础上。 拟议研究的目标是使移动的，无人驾驶系统完全自主，并开发先进的制导，导航和控制系统的自主车辆和其他移动和动态系统的基础上的视觉系统。具体目标是： 1.研究和开发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.研究和开发新的先进制导、导航和控制系统，以及无人驾驶自主移动的飞行器和航空航天机器人以及其他自主系统的算法; 3.为可用于航空航天、医疗和能源应用的移动的机器人和固定机械手开发先进的定位和定位方法： 4.研究和开发用于具有柔性连杆和关节的机器人的引导和定位的先进控制系统。 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（UKF）。应用于卡尔曼滤波器增益的自适应程序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的收敛。这个过程将与一些选定的统计和概率方法进行比较。特别地，将使用扩展的蒙特卡罗方法和粒子滤波方法。所开发的方法、算法和程序将应用于航空航天机器人、移动的机器人和医疗机械手。预计机器人环境感知和控制将取得重大进展。

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.