Intelligent Navigation System Research Articles

Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle

In this paper, an intelligent navigation system for an unmanned underwater vehicle powered by renewable energy and designed for shadow water inspection in missions of a long duration is proposed. The system is composed of an underwater vehicle, which tows a surface vehicle. The surface vehicle is a small boat with photovoltaic panels, a methanol fuel cell and communication equipment, which provides energy and communication to the underwater vehicle. The underwater vehicle has sensors to monitor the underwater environment such as sidescan sonar and a video camera in a flexible configuration and sensors to measure the physical and chemical parameters of water quality on predefined paths for long distances. The underwater vehicle implements a biologically inspired neural architecture for autonomous intelligent navigation. Navigation is carried out by integrating a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro- controller. The autonomous underwater vehicle is capable of operating during long periods of observation and monitoring. This autonomous vehicle is a good tool for observing large areas of sea, since it operates for long periods of time due to the contribution of renewable energy. It correlates all sensor data for time and geodetic position. This vehicle has been used for monitoring the Mar Menor lagoon.

Research on Information Providing Method for Intelligent Navigation System

Background: Today, numerous telematics technologies, i.e., technologies developed by integrating telecommunications with information processing, are applied in vehicles. One such developmental application of this technology to vehicles is to increase the safety or convenience of drivers by providing them with necessary information such as warnings and information on emergencies and traffic situations. However, under certain conditions, there is a high probability of traffic accidents if the driving workload is high. Nowadays, the navigation system is frequently used in the vehicles, this system provides various information including route to the driver. But, the existing navigation systems are not only considered a driver's reaction but also provide unilaterally to the information regardless of them. Such one-side information service type may miss important information to the driver. In addition, it sometimes interferes safety driving. Objective: To solve this problem, the intelligent navigation system needs to the providing way that it checks the driver's reactions after providing information. Namely, if the driver passes the information received from the navigation, then the intelligent system provides more loudly and more frequently. Method: Therefore, in this study we introduce the intelligent navigation system that it automatically controls modality type and its strength when the driver misses or overlooks the information for their safety and entertainment and we analyze the driver's cognitive responses about the modality type and its strength. Results: To evaluate the effectiveness of the proposed system, we analyzed the reaction time and driving workload for each type of the information, modality and its strength. Also we evaluated the users' subjective satisfaction and understanding based on a questionnaire.

The Intelligent Vehicle Navigation System Based on Photoelectric Sensor

This paper describes the intelligent vehicle navigation system based on 16 bits MCU MC9S12XS128. The system uses MC9S12XS128 as control core, realizing path recognition through the laser sensor, using PWM signals to control the actuator steering and motor speed, making the intelligent vehicle tracing along the black line of 25mm wide placidly. This technology can be widely used in automatic warehouse, robot vehicle and unmanned factory, etc. In this case, we designed and developed this intelligent vehicle navigation system, among it the PID algorithm is applied to the control structure. And the photoelectric sensing technology is also applied into the system. This system is of important reference significance for the research on unpiloted system.

INNOVATIVE INTELLIGENT NAVIGATION SYSTEM BY APPLYING FUZZY LOGIC AND CELL ASSEMBLIES IN RESCUING ROBOT

In this study, an intelligent navigation system is developed by using fuzzy logic and Cell Assemblies (CAs) approaches, together with various sensors, in accordance with mimicking human behavior. Because disaster areas can be extremely dangerous and might cover large areas, it is important to make the robot's movement safer and more efficient. Therefore, two models with different functions are designed. The “robot navigation” model is applied to a narrow or dark place by using a laser range finder whereas the “intelligent cognitive” model uses intelligent direction change in an open area by integrating a vision camera. The intelligent cognitive model that is implemented by CAs with fatiguing Leaky Integrate and Fire (fLIF) neurons can not only absorb the ideals of working and long-term memories to explain the phenomenon of biology neurons, but can also imitate human cognitive processes. Additionally, it is not difficult to combine and expand this model to become multifunctional, which mimics human learning. The main contribution of this study is that the clever combination of sensors significantly reduces the frequency of use of vision and image processing. Furthermore, the combined system can avoid potential risks and efficiently shorten the motion time by importing the vision camera. This system has been tested in several simulated environment schemes and the experimental results have proven that it can produce correct action commands relative to different schemes and can also improve the motion path to effectively meet the requirements of disaster relief.

The Application of SIFT Algorithm in Blind Road Environmental Image Matching

Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.

Event classification for vehicle navigation system by regional optical flow analysis

We address the problem of event classification for intelligent vehicle navigation system from video sequences acquired by a front mounted camera in complex urban scenes. Although in normal driving condition, large variety of events could be found and be preferably attached to an alerting system in a vehicle, there have been relatively narrow research activities on driving scene analysis, for example, finding local information such as lanes, pedestrians, traffic signs or light detections. Yet, the above-mentioned methods only provide limited performance due to many challenges in normal urban driving conditions, i.e. complex background, inhomogeneous illumination, occlusion, etc. In this paper, we tackle the problem of classification of various events by learning regional optical flows to detect some important events (very frequent occurring and involving riskiness on driving) using low cost front mounted camera equipment. We approached the problem as follows: First, we present an optical flow-based event detection method by regional significance analysis with the introduction of a novel significance map based on regional histograms of flow vectors; Second, we present a global and a local method to robustly detect ego-motion-based events and target-motion-based events. In our experiments, we achieved classification accuracy about 91% on average tested with two classifiers (Bayesian and SVM). We also show the performance of the method in terms of computational complexity achieving about 14.3 fps on a laptop computer with Intel Pentium 1.2 Ghz.

Intelligent Autonomous Navigation System for the Wheeled Mobile Robot

An intelligent behavior control system for an autonomous mobile robot operating in an unstructured environment with sensor uncertainties is proposed. This study focuses on implementing and improving the methodology from Motlagh et al. [7] on a two-wheeled P3DX mobile robot. Motlagh et al. verified their design with computer simulation. When applying it on a real robot platform, we noticed some problems and improved the design using sensor selection strategy, safe rule and target switching strategy. The proposed sensor fusion architecture introduces two additional sensors, a laser range finder on the robot and an omnidirectional CCD camera on the ceiling, to improve the reliability of the sensing capability of P3DX mobile robot. The target switching strategy is used to guide the robot out of a dead zone and reach the target by creating a virtual target. Results of the experiments with the U-shaped and subspace dead zones are presented. These results proved that the target switching strategy successfully dealt with the loop path problem in the dead zone.

Design of Intelligent Vehicles Control System based on Photoelectric Detector

Given the characteristics of the road information, an intelligent vehicle navigation system based on infrared sensors is developed. Signal collection and processing method are outlined. This paper describes a method cleverly exploiting principles of optics to improve the lane-keeping ability of infrared photoelectric sensors. Considering that the conventional PID can not meet the requirement of motor controls for their real-time character and table lookup based on fuzzy control cannot meet the requirement of steady precision, an improved PID with fuzzy control algorithm is proposed, As a result of test, navigation accuracy is increased by double; dynamic performance and steady precision of the system are both obviously improved by the control algorithm proposed.

An Road Obstacle Detection Method Based on Monocular Measurement

Obstacle detection in the intelligent vehicle vision navigation system occupies a very important role. The studies for the obstacles detecting, especially Monocular Measurement from the computer vision, simplifying monocular vision system to camera projection model. Getting the conversion relation between image coordinate and the world coordinate system through the geometry derivation to establish the measurement model and achieve the obstacle measurement. The experiment proved that the error of this measurement model selected is within the acceptable range.

Tracking feature extraction based on manifold learning framework

Manifold learning is a fast growing area of research recently. The main purpose of manifold learning is to search for intrinsic variables underlying high-dimensional inputs which lie on or are close to a low-dimensional manifold. Different from current theoretical works and applications of manifold learning approaches, in our work manifold learning framework is transferred to tracking feature extraction for the first time. The contributions of this article include three aspects. Firstly, in this article, we focus on tracking feature extraction for dynamic visual tracking on dynamic systems. The feature extracted in this article is based on manifold learning framework and is particular for dynamic tracking purpose. It can be directly applied to system control of dynamic systems. This is different from most traditional tracking features which are used for recognition and detection. Secondly, the proposed tracking feature extraction method has been successfully applied to three different dynamic systems: dynamic robot system, intelligent vehicle system and aircraft visual navigation system. Thirdly, experimental results have proven the validity of the tracking method based on manifold learning framework. Particularly, in the tracking experiments the vision system is dynamic. The tracking method is also compared with the well-known mean-shift tracking method, and tracking results have shown that our method outperforms the latter.

소프트 컴퓨팅을 이용한 지능형 네비게이션에 관한 연구

본 논문은 사용자의 운행 정보를 이용하여 사용자의 선호도 및 성향과 주변의 환경을 판단하고 적용하여 사용자에게 적합한 경로를 추천하는 지능형 네비게이션 시스템을 제안한다. 이 네비게이션 시스템은 센서 정보와 지능형교통시스템의 정보를 이용하여 추천된 경로의 환경 상태와 지형상태를 평가하고 사용자의 감정 상태와 사용자에게 심리적인 영향을 주는 도로의 환경 상태도 고려한다. 또한 소프트 컴퓨팅 기법을 사용하여 인간의 선호도와 성향을 추론 및 학습하며 제안한 알고리즘은 시뮬레이션을 통해 검증한다. In this paper, we propose an intelligent navigation system that selects a proper route for user and applies the user's preference, user's tendency and environmental state estimated by driving information of user and road state. The system uses data of sensors, navigation and intelligent transport system to evaluate conditions of roads and it considers state of user's emotion. The system also uses soft-computing method to infer and learn the user's preference and tendency. We verify the proposed algorithm by computer simulation.

Testing a reliable in-vehicle navigation algorithm in the field

The results of a field experiment carried out to assess the accuracy and efficiency of a new in-vehicle navigation algorithm, whose aim is to incorporate and consider travel time reliability and route the guided vehicle along uncongested roads, in the absence of real-time traffic information are presented. Using historical travel time profiles deduced from floating vehicle data, the algorithm is implemented in a purpose-developed software tool and tested in the London Congestion Charging Zone. The experiment consists of driving a vehicle along routes computed by the algorithm and comparing the outcome with that of a conventional navigation system installed in a second vehicle. The results indicate that the new algorithm outperforms the conventional system in most cases, thus suggesting that it is a step forward towards a more intelligent navigation system.

Navigation and Multimodal Transportation with EasyTransport

Today's intelligent navigation and transportation systems influence the way travelers access information to plan trips efficiently. By and large, such systems derive their navigational suggestions out of precompiled data regarding direct road connections recorded in their databases. They typically facilitate passenger navigation within predefined geographic regions, provide location tracing and direction functionalities over cellular networks or the Web and deliver accurate but not necessarily optimal transition paths. This article proposes EasyTransport, an intelligent navigation and multimodal transportation guidance system that focuses on near-optimal trajectory planning and informed decision making. EasyTransport introduces a number of novel features. The EasyTransport system helps travelers plan the most efficient route and choose the best modes of transportation in urban or large-scale geographic areas. Users can adapt the generated outcomes through an interactive interface.

The Application of Integrated GPS and Dead Reckoning Positioning in Automotive Intelligent Navigation System

The applications of Global Positioning System (GPS) are increasingly widespread in China. GPS positioning is more and more popular. Especially, the automotive navigation system which relies on GPS and Dead Reckoning technology is developing quickly because of the anticipated huge future market in China. In this paper a practical combined positioning model of GPS/DR is put forward. This model designed for automotive navigation system makes use of a Kalman filter to improve position and map matching veracity by means of filtering the raw GPS and DR signals. In practical examples, the validity of the model is illustrated. Several experiments and their results of integrated GPS/DR positioning in automotive navigation system will show that a Kalman Filter based on integrated GPS/DR position is necessary, feasible and efficient for automotive navigation application. Certainly, this combined positioning model, similar to other models, can not resolve all situation issues. In the paper, the applicable principles of the model are given and the advantages and disadvantages of this model are compared with other positioning models. Finally, suggestions are given for further improving integrated GPS/DR system performance, and the application aspects of integrated GPS/DR technology in the automotive navigation system are summarized.

BRIDGING THE GAPS: HYBRID TRACKING FOR ADAPTIVE MOBILE AUGMENTED REALITY

Tracking accuracy in a location-aware mobile system can change dynamically as a function of the user's location and other variables specific to the tracking technologies used. This is especially problematic for mobile augmented reality systems, which ideally require extremely precise position tracking for the user's head, but which may not always be able to achieve that level of accuracy. While it is possible to ignore variable positional accuracy in an augmented reality user interface, this can make for a confusing system; for example, when accuracy is low, virtual objects that are nominally registered with real ones may be too far off to be of use. To address this problem, we describe an experimental mobile augmented reality system that: (1) employs multiple position-tracking technologies, including ones that apply heuristics based on environmental knowledge; (2) coordinates these concurrently monitored tracking systems; and (3) automatically adapts the user interface to varying degrees of confidence in tracking accuracy. We share our experiences with managing these multiple tracking technologies, employing various techniques to facilitate smooth and reasonable “hand-offs” between the cooperating systems. We present these results in the context of an intelligent navigational guidance system that helps users to orient themselves in an unfamiliar environment, using path planning to guide them toward destinations they choose, and sometimes towards ones the system infers as equally relevant.

Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system

This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University.

Solbase: a databank of solutions for occupational hazards and risks.

Several attempts have been made to develop strategies for an effective control of workplace hazards. This paper will focus on the results of a European project called Solbase, which is a databank for solutions to occupational hazards and risks. The Safety Science Group of Delft University of Technology in collaboration with TNO Work and Organisation (formerly NIA-TNO) designed Solbase in a series of projects funded by the Dutch Ministry of Social Affairs and Employment and the European Commission. It consists of the design of and software for a databank with an intelligent navigation system allowing users two principal entry points, which correspond to two basic types of solutions. The first entry point is based on the production process, subdivided into the production principle and production function. This entry point provides the dissemination of solutions within and between branches of industry. The second entry point includes the hazard and its emission and transmission as an access point for more conventional occupational hygiene control measures. With the partners of the consortium, from Spain, Italy, Ireland, Germany, the UK and The Netherlands, 535 new and existing solutions throughout Europe and the world were gathered to test the software and the solutions during a field study. Despite the relatively small number of 'test solutions' used, 54% of the search actions in the field study resulted in a useful and suitable solution which the company could actually put into practice. The companies characterized the software as very user friendly. The reproducibility of the coding system for solutions, the classification tree, was satisfactory. Most coders chose the same keywords from the classification tree to describe a corresponding solution. Solbase is a good searching machine for workplace solutions. Especially, the classification of production processes is an inherent guarantee of an exchange of information across the borders of a specific company or branch of industry.

An intelligent collision avoidance system for AUVs using fuzzy relational products

This paper describes a heuristic search technique carrying out collision avoidance for autonomous underwater vehicles (AUVs). Fuzzy relational products are used as the mathematical implement for the analysis and synthesis of relations between obstacles that are met in the navigation environment and available candidate nodes. In this paper, we propose a more effective evaluation function that reflects the heuristic information of domain experts on obstacle clearance, and an advanced heuristic search method performing collision avoidance for AUVs. The search technique adopts fuzzy relational products to conduct path-planning of intelligent navigation system. In order to verify the performance of proposed heuristic search, it is compared with A ∗ search method through simulation in view of the CPU time, the optimization of path and the amount of memory usage.

An integrated approach in the design of a navigation system for an AUV

An intelligent navigation system for an autonomous underwater vehicle known as Hammerhead is being developed by the University of Plymouth and Cranfield University. The Hammerhead navigation system is an integrated low-cost vision navigation subsystem and an inertial navigation system/global positioning subsystem. It is being developed to demonstrate the feasibility of using an integrated visual navigation system and inertial navigation system to navigate between intermittent global positioning system fixes whilst providing an enhanced imaging and tracking capability. This paper describes the present hardware/sensors composition, software design and preliminary experiment to combine measurement data from all the on-board sensors to derive an estimated position and orientation of the autonomous underwater vehicle during both submerged and surface operations.

DEVELOPMENT OF AN EVENT-REPORTING INTELLIGENT VEHICLE NAVIGATION SYSTEM FOR AREAS WITH URBAN CANYONS

Common vehicle navigation systems employ mostly GPS satellite positioning to track the vehicle's position and velocity. The tracked position can be dynamically shown on a digital map or automatic route guidance can be performed. So-called “Intelligent GPS Vehicle Navigation Systems”, simply speaking, are extensions of common GPS vehicle navigation systems. It can be said, that they are GPS vehicle positioning systems with embedded intelligence. From the authors' point of view, an intelligent system should be able to detect the changes of the real-world situation and to give an appropriate response to the changes. Therefore, an Intelligent GPS Navigation System should have all the basic functionality of a common GPS navigation system, and be able to detect and react to the conditions that are of concern of the driver or the vehicle manager. To achieve this, an intelligent GPS navigation system should be integrated with different types of sensors and communication devices that are able to monitor and perform event reporting to the vehicle's real-word situation. Hence, event reporting is an essential component of an Intelligent Vehicle Navigation System (IVNS). With regard to the navigation part of the system, it is essential that the vehicle's trajectory can be continuously tracked to provide necessary guidance for the driver. However, for GPS-alone systems a reliable position determination in urban canyons may not always be possible, therefore it is necessary to investigate the integration of other vehicle tracking devices and methods.After the discussion of the potential applications of an event reporting IVNS, a more detailed description of a prototype system will be given. Such a system is being developed in a research project at the Department of Land Surveying and Geoinformatics of the Hong Kong Polytechnic University. Finally, field test results of the prototype IVNS conducted in Hong Kong are presented and the integration of wireless location services for positioning and a new GPS-alone position tracking method for urban canyons are briefly discussed.