Current Position Of Vehicle Research Articles

A NOVEL GPS FAULT DETECTION AND EXCLUSION ALGORITHM AIDED BY IMU AND VO DATA FOR VEHICLE INTEGRATED NAVIGATION IN URBAN ENVIRONMENTS

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

The Design and Simulation of Autonomous Agricultural Vehicle for Orchards

In recent years, agricultural producers have faced challenges due to the uncertainty of labor force access, growing demand for safe, accessible, and high-quality agricultural products, high competition with international producers, and the need to reduce their carbon footprint. To continue their competitive and profitable production, producers must invest in technology and increase efficiency. Autonomous agricultural vehicles are crucial for autonomous processes in orchards, increasing productivity, collecting data for decision-making, reducing operating costs, and carbon footprint. This study focuses on the design and simulation of an autonomous vehicle for orchards. The autonomous vehicle can map the orchards using data from odometry and light detection and ranging (LIDAR) sensors by utilizing Simultaneous Localization and Mapping (SLAM) algorithm, accurately determine its position using the Adaptive Monte Carlo Localization method, and avoid obstacles using the dynamic window approach algorithm. The autonomous vehicle is an original design for netted orchards where GPS cannot work properly and is fully autonomous, requiring no external GPS data. It is expected to provide higher efficiency by reducing environmental pollution, operating expenses, and labor force in practice. The success of the mapping and localization application depends on the update frequency of the position and the number of particles used for localization. A path-planning application was developed to reach the desired position from the autonomous agricultural vehicle's current position on the map. The Dijkstra Algorithm was used for path planning, with the Dynamic Window Approach allowing the robot to escape obstacle. The simulation studies achieved the lowest position error when the vehicle's position was updated at intervals of 2 cm, and a minimum of 500 and a maximum of 2000 particles were used. While the vehicle was moving on a straight obstacle row in the simulation environment, an average localization error of 2.1 cm was obtained. This error is convenient as it enables seamless navigation between tree rows without any collision.

PRYORTIE: Public on-demand smart transportation service via a scalable fog-based platform

Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traffic, number of existing drivers en-route, and local weather which may cause a huge delay. Dial-A-Ride Problem with Transfers (DARPT) allows passengers to change vehicles during their trip in a bid to minimize the delay. However, DARPT relies solely on using the existing Global Positioning System (GPS) that suffers from delay in propagating raw traffic data (e.g., route coordinates, geodesic distance, and time stamp) on time and ultimately causes traffic disruption and increases ride time constraint. To this end, this work introduces a novel interactive Fog-based navigation simulator, PRYORTIE. PRYORTIE uses roadside smart units in the form of fog nodes as a means for collecting and sharing real-time traffic data comprising of vehicles’ location and speed within the area, they each cover. These nodes can calculate an optimal route for a vehicle using a novel path recommender algorithm that utilizes a vehicle's current position, its destination, and current traffic data. The nodes can update the route in real-time using the same algorithm if traffic conditions within their radius change. A package delivery case study has been implemented and simulated in PRYORTIE to demonstrate the impact of using fog nodes in routing and navigation. Compared with the state-of-the-art, the proposed approach shows on average 46.34% improvement in saving travel time. The proposed method clearly demonstrates tangible benefits of communicating real-time data to drivers and lays the ground for subsequent future work in smart transportation and navigation especially in the context of last mile delivery. It can easily be extended to autonomous vehicles and can be easily integrated into existing transportation frameworks that employ a fog-cloud paradigm for smart transportation.

Developed Smart Vehicle Tracking System using GPS and GSM Modem

 Fast technological development has streamlined daily living. On the other hand, as a result of a shortage of emergency services, the growth of technology has also increased the incidence of traffic accidents, which cause enormous loss of life and property. Systems for monitoring vehicles and detecting accidents are highly dependable and secure, and they are especially useful for remotely monitoring vehicle activity. In this paper, based on the Global Positioning System (GPS) and the Global System for Mobile (GSM) Communication has been implementing, an effective system for monitoring vehicle movement and identifying any accidents that occur has been established GSM. The suggested method successfully made use of widely available hardware elements that integrate a smartphone and a vehicle unit that is mounted within the car. The proposed system would use General Packet Radio Service (GPRS) to automatically communicate and store vehicle data to the Internet of Things (IoT) every 10 seconds. In addition, the system will create an SMS Short Message Service (SMS) and send it to the user's smartphone in response to an SMS request received with the vehicle's current position information. At the user side, it has been an android smartphone for software application connected to the Thinger.io platform that has been used to view up-to-date car information. Only the authorized user who has access to the Thinger.io platform and the login and password may access this application due to its security. The suggested system's accuracy has been evaluated. The findings show that the suggested method can follow the vehicle high accurately.

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

RegioRail—GNSS Train-Positioning System for Automatic Indications of Crisis Traffic Situations on Regional Rail Lines

The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. The main objective of this article is to introduce (i) a concept of a solution for identification of the current position of rail vehicles based on the worldwide-recognized system of the GNSS with the use of an original railway network data model, and (ii) the use of this concept as supplementary support for the dispatcher control of rail traffic on regional lines. The solution was based on an original, multilayer rail network data model supporting (i) the identification of rail vehicle position and (ii) novel algorithms evaluating the mutual positions of several trains while detecting the selected crisis situation. In addition, original algorithms that enable automatic network model-building (on the database server level) directly from the official railway infrastructure database were developed. The verification of the proposed solutions (using rail traffic simulations) was focused on the evaluation of (i) the changing mutual positions (distances) of trains on the railway network, (ii) the detection of nonstandard or crisis traffic situations, and (iii) the results of the calculations of necessary braking distances of trains for stopping and collision avoidance. The above verification demonstrated the good applicability of the proposed solutions for the potential deployment within supplementary software support for real traffic control. The described concept of the supplementary support determined for railway traffic control (using the localization of trains by means of the GNSS) is intended mainly for regional, single-rail lines. This type of line is very often not sufficiently equipped with standard signaling and interlocking equipment to ensure the necessary traffic safety. Therefore, when deploying this support, the new algorithms for the automatic detection of critical traffic situations represent a significant potential contribution to increasing operational safety.

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motor-driven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim®, shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.

Underwater Docking Approach and Homing to Enable Persistent Operation.

One of the main limiting factors in deployment of marine robots is the issue of energy sustainability. This is particularly challenging for traditional propeller-driven autonomous underwater vehicles which operate using energy intensive thrusters. One emerging technology to enable persistent performance is the use of autonomous recharging and retasking through underwater docking stations. This paper presents an integrated navigational algorithm to facilitate reliable underwater docking of autonomous underwater vehicles. Specifically, the algorithm dynamically re-plans Dubins paths to create an efficient trajectory from the current vehicle position through approach into terminal homing. The path is followed using integral line of sight control until handoff to the terminal homing method. A light tracking algorithm drives the vehicle from the handoff location into the dock. In experimental testing using an Oceanserver Iver3 and Bluefin SandShark, the approach phase reached the target handoff within 2 m in 48 of 48 tests. The terminal homing phase was capable of handling up to 5 m offsets with approximately 70% accuracy (12 of 17 tests). In the event of failed docking, a Dubins path is generated to efficiently drive the vehicle to re-attempt docking. The vehicle should be able to successfully dock in the majority of foreseeable scenarios when re-attempts are considered. This method, when combined with recent work on docking station design, intelligent cooperative path planning, underwater communication, and underwater power transfer, will enable true persistent undersea operation in the extremely dynamic ocean environment.

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

Lane-Marker-Based Map Construction and Map Precision Evaluation Methods Using On-Board Cameras for Autonomous Driving

This report describes a map construction and evaluation method based on lane-marker information for autonomous driving. Autonomous driving systems typically require digital high-definition (HD) maps to correct the current position of autonomous vehicles by using localization techniques. However, an HD map is usually costly to generate because it requires a special-purpose vehicle and mapping system with precise and expensive sensors. This report presents a map construction method that uses cost-efficient on-board cameras. We implement two types of map construction methods with two different cameras in terms of range and field of view and test their performances to determine the minimum sensor specification required for autonomous driving. This report also presents a constructed map evaluation method to determine the “usability” of the map for autonomous driving. Given that the system cannot obtain “true” positions of landmarks, the method judges whether the constructed map contains sufficient information for localization via the presented indices “lateral-distance error.” The methods are verified based on mapping and localization errors determined via manual driving tests. Furthermore, the smoothness of steering maneuvers is determined by conducting autonomous driving tests on a proving ground. The results reveal the necessary conditions of sensor requirements, i.e., the constant visibility of landmarks is one of the key factors for ego-localization to conduct autonomous driving.

An Improved Heterogeneous Motion Prediction Algorithm for Vehicle-to-Vehicle Message Ferrying in VANETs

In infrastructure-less ad-hoc networks, the nodes can converse directly with other nodes without centralized servers. The vehicular networks are derived from the features of mobile ad-hoc networks (MANETs) whereas the message transmissions between the nodes are direct with each other or through some fixed terminals. But, a unique trademark is that the vehicle-to-vehicle ad-hoc links are poor because of extreme mobility which leads to frequent route breakage that causes emergency message delivery failure. The routing protocols availed in MANETS does not suit due to quick topology changes. The hop selection is based on the best cumulative communication duration that will be failing in some cases. Also, the worst/average case scenario is that the minimum connectivity to the source vehicle is not described in existing approaches. The core idea about the proposed research is to deliver data through an optimum path and to eliminate rupture link during the packet transmission. The system uses the Heterogeneous Motion Guess (HMG) algorithm for vehicular networks to set up a vested path. The HMG will select the relay vehicle's current position is near to the target vehicle plus the vehicle will opt the relay forwarder that has good radio coverage. The link lifetime of the selected hop should be greater than the minimum connectivity duration with the source vehicle. The selected vehicle will append the link expiry time and minimum connectivity duration in Route Demand Dispatch message in a unicast manner. This approach significantly minimizes the hop count numbers and improves the stability of the path. It radically predicts the link life, so the node can decide an alternative vested route. The simulation result shows how well the reactive demand-based HMG affords optimum results in terms of packet transmission success rate, average minimum hop counts, and communication delay.

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

A Novel Pure Pursuit Algorithm for Autonomous Vehicles Based on Salp Swarm Algorithm and Velocity Controller

Pure pursuit algorithm is one of the most effective ways of path tracking in autonomous vehicles. Nevertheless, the tracking accuracy of the existing pure pursuit algorithm is limited by the look-ahead distance. In this paper, to improve the tracking accuracy of the pure pursuit algorithm, a novel pure pursuit algorithm based on the optimized look-ahead distance named OLDPPA is proposed. Four improvements are presented in OLDPPA. Firstly, to find a better look-ahead distance of pure pursuit algorithm, salp swarm algorithm (SSA) is used in pure pursuit algorithm. Secondly, Brownian motion, a random motion mechanism of particles, is introduced in SSA to enhance its exploitation and exploration capabilities. Thirdly, to accelerate the convergence speed of SSA, a weighted mechanism which uses two different weights in the search process to adjust the salps closer to the food source quickly is assigned. Based on innovations 2 and 3, adaptive Brownian motion salp swarm algorithm (ABMSSA) is proposed and applied to pure pursuit algorithm. Finally, a velocity controller which outputs the speed of the next moment according to the distance and time interval between the look-ahead point and the current vehicle position is designed in OLDPPA, to ensure that the vehicle reaches its destination at a specified time. To verify the effectiveness and efficiency of OLDPPA, OLDPPA is applied in four different paths and the corresponding results are compared with other pure pursuit algorithms that use different look-ahead distances. Experimental results show that the tracking accuracy of OLDPPA is better than other algorithms.

Vehicle Speed Prediction Using a Markov Chain With Speed Constraints

Modern vehicles are designed to improve fuel consumption while satisfying emissions regulations. As a result, powertrains are becoming increasingly complex and changing rapidly. Optimal control based on the future vehicle speed is one way to address these changes. In this approach, accurate prediction of velocity is closely related to the performance of optimal control. However, there exists uncertainty and complexity in predicting the driver’s behavior, which can be influenced by the surrounding driving environment. In order to overcome such limitations, we propose a prediction algorithm of vehicle speed based on a stochastic model using a Markov chain with speed constraints. The Markov chain, which forms the basis of the proposed algorithm, generates the velocity trajectory stochastically within speed constraints. The constraints are estimated by an empirical model that takes into account the road geometry and is organized by the intuitive form of matrices. To reduce the complexity of the vehicle position and roadway integration, a curvilinear coordinate system is presented using GPS information and a roadway geometry model. Based on these coordinates, the algorithm predicts the future velocity for each cycle from the current vehicle position up to the ahead distance. The proposed algorithm is evaluated through experiments in an urban area, and the test vehicle collects driving data on the roadway position domain. The algorithm was verified in the following conditions: free of surrounding vehicles and traffic lights, the average velocity of the test vehicle is 47.8 km/h, and the maximum velocity of the test vehicle is 66.6 km/h. The experimental results show that a 3.8041 km/h root-mean-square-error is achieved with a prediction horizon of up to 200 m.

Detection and Tracking of Vehicles Based on Colour Probability Density

Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.

Geographical Information Enhanced Cooperative Localization in Vehicular Ad-Hoc Networks

Cooperative localizer is a potential positioning technique for vehicular ad-hoc networks (VANETs). However, it would suffer from the non-line-of-sight (NLOS) problems widely existing in VANETs. This letter proposes a geographical information enhanced cooperative localizer (GIE-CL) for VANET with time-of-arrival (TOA) measurements. It iterates between NLOS identification and extended generalized approximate message passing (EGAMP) motivated cooperative positioning. A region sampling method is developed to identify NLOS measurements based on geographical information and current vehicle position estimations. Subsequently, the detected NLOS measurements are removed and the EGAMP localizer is activated to re-estimate the vehicle positions. The above-mentioned iteration will be terminated until convergence is reached. Initial positions are provided by Global Navigation Satellite System (GNSS). Simulation results show that GIE-CL can handle the NLOS problem, and approach to its performance upper bound provided by the case with known NLOS/LOS link-type information. Compared to EGAMP localizer, the positioning accuracy of GIE-CL is improved by eight times when the allowed localization error is less than 5 m.

Integration of vision and topological self-localization for intelligent vehicles

Self-localization is a crucial task for intelligent vehicles. Conventional localization methods usually suffer from different limitations, such as low accuracy and blind areas for Global Positioning System (GPS), high cost for Inertial Navigation System (INS), and low robustness for vision Simultaneously Localization and Mapping (vSLAM). To overcome these problems, this study proposes a low-cost yet accurate localization method for intelligent vehicles, which only needs a monocular camera and a GPS receiver. First, the proposed method offers multiple feature spaces which are designed from GPS data, localization prediction model, image holistic features, and image local features. Each feature space, from which one candidate set is derived, can make qualitative localization achieved independently. Afterwards, we propose a novel method called K-Nearest Neighbors from Multiple Feature Spaces (KNN-MFS) to fuse these candidate sets. The closest node to the current vehicle position is drawn from the visual map to achieve image-level localization. Finally, the vehicle pose in the visual map, computed by metric localization, can further enhance the localization accuracy. The advantage is that when GPS signals are unavailable at times, the method can still achieve short-range localization by using other feature spaces. The proposed method has been validated with the actual data sets and public data sets. The actual data sets are collected along an industrial park and a rural urban fringe in Wuhan City, China, covering different times and weather conditions, and the total lengths of these routes have to be more than 8 km. The public data sets are Karlsruhe Institute of Technology and Toyota Technology Institute (KITTI). Experimental results show that the proposed method can adapt to different times and weather conditions with good robustness in varying environments, and the localization errors are less than 35 cm in all the tests in average. Experimental results on the same routes without GPS data are also reported, which demonstrate that the proposed method can achieve comparable localization accuracy.

Insurance Telematics Using GPS Tracker and Smartphone

Insurance telematics is widely used in Europe by young and first-time car drivers[1] [1]. It relies on an insurance premium that is based not only on static measures, but also on dynamic measures. The dynamic measures are the current position of vehicle, the current speed at that position, time spent on the road, the driver’s style of driving etc. When we talk about insurance telematics, we refer to insurance schemes pay-as-you-drive (PAYD), pay-how-you-drive (PHYD) and manage-how-you-drive (MHYD). Telematics could be smartphone based insurance with technology which relies on insurance premiums that reflect the risk profile of drivers and the traditional in-car mounted devices for insurance telematics. This telematics work is experience from a recent insurance telematics validation and analyst pilot run in England on motorway around the Heathrow airport. In this telematics test, the global positioning system (GPS) and smartphone is used as receiver.The small global positioning system (GPS) transmitter is a small tracker in-car mounted devices with SIM card with is set up for sending SMS to smartphone every ½ minutes. The smartphone is the receiver equipment which is used to collect telematics data which is ready for analysis. There are many companies around with data analytics expertise who use the latest big data analytics technology such as Hubio Dynamic Data Warehouse[2] [2]. They analyse data from a wide range of different data sources, including databases, sensors, smartphones and social media.

Real-time trajectory tracking of an unmanned aerial vehicle using a self-tuning fuzzy proportional integral derivative controller

In the present study, a desired reference trajectory was autonomously tracked by means of a quadrotor unmanned aerial vehicle with a self-tuning fuzzy proportional integral derivative controller. A proportional integral derivative controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize the quadrotor, to control the attitude and to track the trajectory. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of unmanned aerial vehicle and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which produce pitch, roll, yaw and height values. The fuzzy proportional integral derivative control algorithm is real-time applied to the quadrotor in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and self-tuning fuzzy proportional integral derivative controller have accomplished to track a defined trajectory with the aircraft, the self-tuning fuzzy proportional integral derivative controller has been able to control with less errors than the classical proportional integral derivative controller.

Autonomous underwater vehicle navigation using a single tomographic node

This study investigates AUV navigation using the one-way travel-time from an ocean tomographic sensor node. For the study of ocean acoustic tomography, several tomographic nodes are moored in the study area and broadcast m-sequence signals periodically. Each moored node consists of an acoustic transceiver, a GPS module, and a processing unit. The pulse-per-second signal from the GPS module is used to ensure the time synchronization of all the moored nodes. A tomographic sensor is installed on an AUV, serving as a moving node. To obtain highly accurate one-way travel-time between moored nodes and AUV, a chip scale atomic clock is operated on the AUV. Based on dead-reckoning and travel-time measurement, Extended Kalman Filter (EKF) is employed to improve the AUV localization. An experiment was conducted using an AUV and one moored node deployed in a shallow water environment southwest of Taiwan. The measurements from a Doppler velocity logger and a compass are used to calculate the current position of vehicle. The AUV was operated near the surface to obtain the GPS position as the ground truth for the performance evaluation. Preliminary results show the RMS position error between the EKF prediction and ground truth is about 80 m.