An Intelligent Integrated Navigation and Autopilot System for Uninhabited Surface Vehicles

全球定位系统（GPS）是民用和军用车辆系统的非常有用的导航辅助设备。然而，由于卫星无线电信号微弱，在河流、山脉和峡谷等狭窄地区开展行动时，很容易丢失信号，在极端紧张或冲突时期，极易受到恐怖分子或侵略者的蓄意干扰。因此，考虑到GPS对信号丢失的脆弱性，在设计自动驾驶汽车（AV）的导航子系统时，不要完全依赖它们。这个问题的一个解决方案是用基于同时定位和映射（SLAM）技术的算法来增强子系统。SLAM是同时构建操作环境的基于特征的地图并利用其来估计AV的位置的过程。为了进一步提高无人机的能力和性能包线，将SLAM增强导航子系统与自适应控制子系统相结合，从而将它们转化为一个完全集成的系统是合适的。现有的智能导航（IN）子系统将补充一个SLAM算法，这将是新设计的，也能够与其他类型的更传统的导航系统接口。新改进的IN子系统将以现有的导航子系统和一个由Atlantic Inertial Systems（AIS）提供的惯性测量单元增强的导航系统为基准，AIS是该项目的工业合作者。虽然智能集成系统将被设计和开发用于海洋应用，但所发展的技术将能够被转移并用于其他类型的AV。许多SLAM算法的一个共同特征是依赖于扩展卡尔曼滤波器来充当信息数据收集机制。在这项研究中，建议采用区间卡尔曼滤波器（IKF），它使用区间演算在其设计中，而不是增强使用人工智能技术来构建一个模糊IKF（FIKF）。用于SLAM的场景信息提取可以来自视觉或非视觉源。视觉SLAM具有选择和使用从局部场景的视频图像获得的鲁棒特征的益处。这项工作的一个新的方面将是设计一个功能匹配算法（FMA），将能够在夜间条件下工作。因此，一个信息数据收集机制的基础上FIKF将与FMA形成整体SLAM增强algorithm.While有一些方法引入到自动驾驶仪设计的适应性，在本研究项目中，将采取的方法将基于在线闭环识别和模型预测控制的组合。因此，本文描述的方法代表了一个新的概念框架的设计海洋自动驾驶仪。还应指出的是，迄今为止，所有无人驾驶海洋车辆系统识别试验都是在开环中进行的。在成功完成自适应自动驾驶仪的设计后，它将与SLAM增强型IN子系统合并，形成IINA系统。

Global positioning systems (GPSs) are very useful navigational aids for both civilian and military vehicular systems. However, owing to the weakness of the radio signals from the satellites they are susceptible to signal loss when operations are being conducted in confined areas such as rivers, mountains and canyons, and are extremely exposed to being deliberately jammed by terrorists or aggressors during times of extreme tension or conflict. Thus given the vulnerability of GPSs to signal loss, it is prudent not to be totally reliant upon them in the design of navigation subsystems for autonomous vehicles (AVs). One solution to this problem is to enhance the subsystem with an algorithm based on simultaneous localization and mapping (SLAM) techniques. SLAM being the process of simultaneously building a feature based map of the operating environment and utilizing it to estimate the location of an AV. To further improve the capability and performance envelope of an AV it is appropriate to combine a SLAM reinforced navigation subsystem with an adaptive control subsystem thereby transforming them into one fully integrated system.This research proposal aims to design and build a new advanced intelligent integrated navigation and autopilot (IINA) system with adaptive capabilities for uninhabited surface vehicles (USVs). The existing intelligent navigation (IN) subsystem will be complemented with a SLAM algorithm which will be newly designed and also capable of interfacing with other types of more traditional navigation system. The new improved IN subsystem will be benchmarked against the existing navigation subsystem and a navigation system enhanced with an inertial measurement unit supplied by Atlantic Inertial Systems (AIS) who are the industrial collaborator for this project. Whilst the intelligent integrated system will be designed and developed for a marine application, the technology evolved will be able to be transferred and used in other types of AV.A common feature with many SLAM algorithms is the reliance upon extended Kalman filters to act as the information data collection mechanism. In this research proposal an interval Kalman filter (IKF) which uses interval calculus in its design will be employed instead and enhanced using artificial intelligence techniques to construct a fuzzy IKF (FIKF). Scene information extraction for SLAM can be from visual or non-visual sources. Visual SLAM has the benefit of selecting and using robust features gained from video imagery of the local scene. A novel aspect of this work will be the design of a feature-matching algorithm (FMA) that will be capable of operating in night-time conditions. Thus, an information data collection mechanism based on a FIKF will be integrated with the FMA to form the overall SLAM enhancement algorithm.Whilst there are a number of methods for introducing adaptability into an autopilot design, in this research project the approach to be taken will be based on a combination of on-line closed loop identification and model predictive control. Thus the methodology described herein represents a new conceptual framework for the design of marine autopilots. It should also be noted that, to date, all uninhabited marine vehicle system identification trials have been performed in the open loop. Upon the successful completion of the design of the adaptive autopilot, it will be merged with the SLAM enhanced IN subsystem to form the IINA system.