Development of a simple navigation system for the Autosub autonomous underwater vehicle

Military Autonomous Underwater Vehicle (AUV) is utilized to search a mine under the sea. This paper presents design and performance verification of real-time hybrid navigation system for AUV. The navigation system uses Doppler Velocity Log (DVL) integration method to correct INS error in underwater. When the AUV is floated on the water, the accumulated error of navigation algorithm is corrected using position/velocity of GPS. The navigation algorithm is verified using 6 Degree Of Freedom (DOF) simulation, Program In the Loop Simulation (PILS). Finally, the experiments are performed in real sea environment to prove the reliability of real-time hybrid navigation algorithm.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

In underwater environment it is not easy to predict all situations and phenomena such as current force, sudden change of temperature and so on. Installation of a proper scheme to cope with un-expected troubles is, therefore, essential when Autonomous Underwater Vehicles (AUVs) carry out their mission out of human's reach. In order to supervise whether the vehicle operates itself in a appropriate way, this paper proposes a model based approach to the self diagnosis for AUVs. This system includes self diagnosis which is carried out based on a dynamics model of an AUV and an active mechanism to get desirable information for diagnosis. The dynamics model is constructed by artificial neural networks taking advantage of its flexible learning ability. When a sensor is found to be defective, dead reckoning using the corresponding output of the dynamics model can be introduced.The performance of the proposed system was examined by implementing it to “The Twin-Burger”, a test-bed AUV. It is shown that the system detects failures of onboard sensors and actuators, and then select proper action schemes to minimize the damage to the AUV.

This paper presents an acoustic localization system for small and low-cost autonomous underwater vehicles (AUVs). Accurate and robust localization for low-cost AUVs would lower the barrier toward multi-AUV research in river and ocean environments. However, these AUVs introduce size, power, and cost constraints that prevent the use of conventional AUV sensors and acoustic positioning systems, adding great difficulty to the problem of underwater localization. Our system uses a single acoustic transmitter placed at a reference point and is acoustically passive on the AUV, reducing cost and power use, and enabling multi-AUV localization. The AUV has an ultra-short baseline (USBL) receiver array that uses one-way travel-time (OWTT) and phased-array beamforming to calculate range, azimuth, and inclination to the transmitter, providing an instantaneous estimate of the vehicle location. This estimate is fed to a particle filter and graph-based smoothing algorithm to generate a consistent AUV trajectory. We describe the complete processing pipeline of our system, and present results based on experiments using a low-cost AUV. To the authors' knowledge, this work constitutes the first practical demonstration of the feasibility of OWTT inverted USBL navigation for AUVs.

A mathematical description of the attitude and navigation system of autonomous unmanned underwater vehicles in mid-latitudes using Poisson’s equations is considered. For the proposed mathematical description, a graphical application is being developed using the Qt cross-platform library, which makes it possible to determine the orientation and navigation parameters of an autonomous unmanned underwater vehicle.

Owing to the complex operating environment of underwater vehicles, many uncertainties of sensors data, big noises of sensors , low precision and high rate of wild points of underwater acoustic sensors, data processing of motion sensors data for underwater vehicle navigation system becomes extremely important. The integrated navigation system of autonomous underwater vehicle based on dead-reckoning is introduced. An modified adaptive Kalman filter is adopted for underwater vehicle sensors information data processing. Experimental results show that the modified self-adaptive Kalman filter(SAKF) is effective, and can meet the underwater robots perform a variety of tasks in the navigation and positioning accuracy..

This work presents an improvement of an inertial-Doppler underwater navigation system for unmanned underwater vehicles (UUV) using a complementary range sonar. The inertial-Doppler navigation system is generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL) accompanying a magnetic compass and a depth sensor. Although conventional navigation systems update bias errors of the inertial sensors, scale effects of the DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model with additional range sonar to improve the performance of the IMU-DVL navigation system for underwater vehicles. The proposed navigation model based on inertial navigation systems includes the scale effects, the bias errors of the DVL, and the error model of the range sonar, where the order of the system states is 21. An extended Kalman filter was adopted to propagate the error covariance, updated the measurement errors and correct the state equation when the external measurements are available. Simulation conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode illustrates the effectiveness of the IMU-DVL navigation system assisted by the additional range measurement.

In autonomous underwater vehicle (AUV) chemical plume tracing applications, the mission objective is to trace chemical plumes to the chemical source in a turbulent flow environment and to accurately estimate the chemical source location. AUV's capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants. A typical AUV chemical plume tracing system includes an adaptive mission planner (AMP) that rapidly responds to the sensor inputs to generate a trajectory for the AUV to trace the plume. Because the AUV has velocity (< 2 m/s) and heading rate (< 10 degree/s) constraints and the vehicle navigation system has navigation fixes, a guidance system is necessary for the AUV to generate heading and speed commands within the constraints to achieve the trajectory desired by the AMP. This article describes the development and field test results of the guidance system for a REMUS AUV to find a chemical plume, trace the chemical plume to its source, maneuver to reliably declare the source location, and map the plume source area after declaration of the source location. Three sets of in-water field tests with the Navy successfully demonstrated the plume tracing and post-declaration mapping capability on a REMUS AUV with the guidance system described herein

Three-dimensional visual simulation is very helpful during the research and development of autonomous underwater vehicle. In this article, a three-dimensional visual simulation system for autonomous underwater vehicle is designed based on an underwater simulator called UWSim. Firstly, underwater scene and the vehicle model is configured, and the dynamic model is built based on the autonomous underwater vehicle. A power reaching law based sliding mode controller is then designed for verification. Finally, a co-simulation platform based on UWSim and Matlab/Simulink is implemented. On this co-simulation platform, a trajectory tracking simulation based on the proposed control strategy is carried out. The simulation results indicate that this three-dimensional visual simulation platform is a helpful method to display and update the visualization scene of autonomous underwater vehicle and verify specific control algorithms.

Model-based adaptive control system for autonomous underwater vehicles

Applied Remote Technology has recently concluded a two-year research and development program to extend the performance of the adjustable diversity acoustic telemetry system (ADATS), which was designed as an acoustic command and control link for autonomous underwater vehicles (AUVs). The result of this program was a second-generation ADATS design, called ADATS II, that uses digital signal processing (DSP) technology. The author presents the results of this development effort and the sea trials conducted on the XP-21 AUV. DSP technology allows the use of complex signal processing algorithms in real time. ADATS II can achieve data rates up to 2500 bits per second with an operational range, depending on data rate, of over 4000 yards.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This work proposes a design method for the propulsion system of autonomous underwater vehicles (AUVs) based on the to be accomplished tasks or trajectories. The method adapts to AUVs involving both fixed and vector thrusters. It is based on the inverse kinematic and dynamic models of the AUV performing a task of trajectory tracking. We first determine the actuation requirement in terms of propulsion effort according to the required task. These efforts are thus used to generate propulsion capabilities in the form of a “Thrust configuration matrix” (TCM). This matrix depends on the positions and orientations of the thrusters, but also thrust controlled angles of vector thrusters. In general, this matrix is compatible with any desired Cartesian trajectory and may include fixed or vector thrusters. From this matrix can be extracted the number, the type, position and orientation of thrusters constituting the obtained propulsion system. Thus, the robot's propulsion capabilities will match the characteristics of the to-be-followed trajectory.

Enhancing underwater thruster anomaly detection with support vector glow encoding description

This paper describes a novel approach to the development of a learning control system for autonomous underwater vehicles (AUV) which presents the AUV as a baby-that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allow it to build this knowledge from the environment itself. The learning techniques are rooted in a nested hierarchical algorithm molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation, it creates schemata (rules) that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata using generalization, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature. >