Autonomous flight and remote site landing guidance research for helicopters

Automated low-altitude flight and landing in remote areas within a civilian environment are investigated, where initial cost, ongoing maintenance costs, and system productivity are important considerations. An approach has been taken which has: (1) utilized those technologies developed for military applications which are directly transferable to a civilian mission; (2) exploited and developed technology areas where new methods or concepts are required; and (3) undertaken research with the potential to lead to innovative methods or concepts required to achieve a manual and fully automatic remote area low-altitude and landing capability. The project has resulted in a definition of system operational concept that includes a sensor subsystem, a sensor fusion/feature extraction capability, and a guidance and control law concept. These subsystem concepts have been developed to sufficient depth to enable further exploration within the NASA simulation environment, and to support programs leading to the flight test

Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mode Controller

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn‟t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Integrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety.publishedVersio

FAA/NASA Joint University Program for Air Transportation Research, 1992-1993

The research conducted during the academic year 1992-1993 under the FAA/NASA sponsored Joint University Program for Air Transportation Research is summarized. The year end review was held at Ohio University, Athens, Ohio, 17-18 June 1993. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology, Ohio University, and Princeton University. Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance, and control theory and practice, aircraft performance, human factors and air traffic management. An overview of the year's activities for each university is also presented

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Joint University Program for Air Transportation Research, 1990-1991

The goals of this program are consistent with the interests of both NASA and the FAA in furthering the safety and efficiency of the National Airspace System. Research carried out at the Massachusetts Institute of Technology (MIT), Ohio University, and Princeton University are covered. Topics studied include passive infrared ice detection for helicopters, the cockpit display of hazardous windshear information, fault detection and isolation for multisensor navigation systems, neural networks for aircraft system identification, and intelligent failure tolerant control