Observability analysis for cooperative navigation system in autonomous underwater vehicles

Near underwater structures or the surface of shallow water, acoustic sensing based navigation of autonomous underwater vehicles (AUVs) suffers from inaccurate positioning which is caused by acoustic sensing such as multi-pass and noisy data. In the paper a new underwater navigation method for AUVs is proposed based on artificial underwater landmarks which are recognized by a vision system implemented in the robot. An underwater image processing strategy and an underwater vision environment analysis method are introduced to improve reliability of the underwater vision system. Experiments were carried out to demonstrate the efficiency of the proposed navigation method of AUVs.

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

One-way-travel-time (OWTT) acoustic ranging has received considerable attention as improvements to acoustic modems and electronic clocks have made it a feasible navigation tool. This paper reports the results of simulations investigating the effect of utilizing inter-vehicle ranging for autonomous underwater vehicle (AUV) navigation. In these simulations, a fleet of AUVs operates in shallow water with a pair of fixed transponders. A rigid timing cycle for acoustic communications was implemented with a message queuing approach to simulate the handicaps of underwater acoustic communication. Furthermore, a simple path following algorithm was used to navigate the AUVs through a waypoint course, and a kinematic motion model was used to simulate AUV movement. The position of each vehicle in the fleet was estimated independently by combining the propagation steps of an extended Kalman filter with the update equations of an extended information filter. Results of the simulations showed the addition of inter-vehicle ranging improved accuracy by 1–2 cm when navigating using four fixed transponders and by 9–24 cm when navigating using two fixed transponders.

We, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), are developing an autonomous underwater vehicle (AUV) whose main mission is monitoring a site at the sea bottom for the carbon dioxide capture and storage (CCS). The AUV cruises very near the sea bottom, and is equipped with chemical sensors in order to detect escape of CO2 from sub-bottom. Of course, the position information of the AUV is critical information for the monitoring. In this paper, a conceptual design of navigation of the AUV is described. Recently, navigation of AUV is implemented by integrating multiple navigation devices including inertial navigation system (INS), Doppler velocity log (DVL), depth sensor, acoustic navigation system, and others. The AUV under construction will be equipped with these navigation sensors, and will integrate those sensors’ outputs to navigate herself. In order to measure the absolute position of the AUV the acoustic method is one of fundamental technique. At the first step of development of the AUV, three acoustic methods are considered to adopt. The three methods are super short baseline (SSBL) method which is a tracking from support ship or other surface station, long baseline (LBL) which is navigation based on preplaced acoustic transponders, and virtual LBL (VLBL) which is navigation based on only single transponder. These acoustic methods are integrated with the navigation result of INS, depth sensor, and DVL. The three methods are used in each appropriate case. Which feature of observation is desired simplicity, accuracy, or independence from support ship and time efficiency? The acoustic method is influenced by environment, and also output of other sensors is depending on the environment, for example the DVL miss the data when the terrain is with many up-hills and down-hills. The integration or filtering parameters of the navigation should be adjusted depending on the influential environmental factor.

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

With the increasing exploration of the marine resources, cooperative navigation (CN) of multi autonomous underwater vehicles (AUVs) can improve exploration efficiency and accuracy. In order to improve the positioning accuracy in the CN, we propose the Information Entropy (IE) algorithm, which can evaluate the measurement informations, and choosing the optimal measurement informations to update the positions of the AUVs, decreasing the influences of the low quality measurement informations to the CN. In this paper, we study the influences of choosing different AUVs' states and the amounts of the measurement informations, and we consider two scenarios, the first is that AUVs equip the equal accuracy navigation equipment; the second is that AUVs equip the different accuracy navigation equipment, which there are several AUVs have the high accuracy equipment, and the others have the low accuracy equipment. Combining with the nonlinear filtering technique, we find using the IE algorithm can reduce the positioning errors of the AUVs compared with selecting measurement informations randomly by the simulation results.

In this paper, we propose a state estimation method for navigation of multiple Autonomous Underwater Vehicles (AUVs) with limited communication traffic. Several positioning methods for a single hovering type AUV have been proposed in order to achieve different kind of missions. We had proposed a navigation method of multiple AUVs aimed at unexplored and wide seafloor surveys. In this method, the AUVs need to share their estimated states. However, typical data rates of acoustic communications in underwater environments are too small to share the states with each other constantly. In order to overcome the problem, we propose a state estimation method where the stochastic state estimator “Particle Filter” is applied to multiple AUVs. The information about the estimated states is approximated by “Particle Clustering” using a clustering method (k-means) and a model evaluation method by AIC (Akaike Information Criterion) to select the optimal size of clusters and reduce the required communication data size. Through simulations, the proposed method succeeded in stable positioning and reduction of the data size required to share.

The primary objective of this work is to solve the problem that the long baseline (LBL) positioning system has poor real-time positioning results during the navigation for autonomous underwater vehicles (AUVs). In this paper, it is proposed to use the unscented Kalman filter (UKF) to enhance the navigation and positioning accuracy of the AUV, using the speed information measured by the Doppler Velocity Log (DVL) and the ranging information obtained by the acoustic beacons. Utilizing the unscented transformation to perform integrated navigation, it can avoid linearization truncation errors and Jacobi matrix computation compared with extended Kalman filtering (EKF). The experimental results verify that in the multi-beacon aided AUV integrated navigation system, compared with the conventional LBL and EKF, UKF provides more stable and accurate position information in real time.

Single-beacon navigation for group autonomous underwater vehicles (AUVs) suffers from long navigation period and asynchronization. This paper addresses the problem by introducing the single-input-multiple-output (SIMO) model, where, a single AUV transmits the signal and other AUVs receive the signal. Utilizing the time-delay of signals from the beacon and the sender, the navigation for all AUVs is achieved simultaneously, and the navigation period is dramatically decreased. Moreover, based on the new SIMO model, a tracking method utilizing extended Kalman filter is also put forward to increase the navigation precision and reduce the computational burden. Finally, a coordinate fusion algorithm based on the location, error, and time-delay is presented, which fuses the independent AUV coordinates to be a higher precision holistic coordinate system for group AUVs. Simulation results show the effectiveness of the proposed method.

Bathymetric terrain maps generated from acoustic data offer an attractive alternative for reducing the submerged pose error estimates for autonomous underwater vehicles (AUVs). The goal of this work is to determine the extent of improvement in the navigational accuracy of an AUV equipped with an echo sounder for near-seafloor, shallow water applications. Given bathymetric variations of a certain terrain, this paper analyzes the best achievable positioning accuracy for AUVs. To counter for the strong non-linearity and the non-Gaussian nature of the problem, an optimal Bayesian estimator is initially derived. The fundamental limitations in the pose uncertainty using this approach is encompassed by the Posterior Cramer-Rao bound (PCRB), that is interpreted in terms of the sonar sensor accuracy and the bathymetric variations. The PCRB on the position error covariance is determined and it is shown that the Bayesian Bootstrap filter closely follows this bound using real inferometric sonar data.

In recent years, precious observations by autonomous underwater vehicle (AUV) have been performed. Tracking and navigation of AUV are important for precious observations. In Japan agency for Marine-earth Science and Technology (JAMSTEC) the observations with AUV ldquoURASHIMArdquo have been implemented in deep ocean area. AUV URASHIMA is navigated by an inertial navigation system (INS) mainly. The absolute position of AUV is tracked on the support vessel with super short base-line (SSBL) method of acoustic positioning. And sometime the own position in AUV is updated by the SSBL estimation result to correct the drift error of INS. However the SSBL is not so accurate. So the position correction includes the SSBL error and the position correction is performed drastically. In this paper, a method for accurate tracking of AUV and rapid correction of the INS status is proposed. The tracking is mainly achieved by SSBL on the support vessel. Additionally the INS data is transmitted from AUV including the acoustic positioning signal. The INS output of relative position to past own position is very accurate within short time. The INS data is used for tracking. Furthermore the position result estimated on the vessel is transmitted to AUV with next interrogation signal. And the own position in AUV is rapidly updated with accurate estimation. Some digital data including INS data is modulated with phase shift keying (PSK) method and direct sequence spread spectrum (DSSS) technique for transmission with acoustic positioning signal. The proposed method was considered by simulation. And good results were obtained.

An H∞ controller combined with an Artificial Potential Field Method (APFM) was applied to seabed navigation for Autonomous Underwater Vehicles (AUVs), aimed particularly at obstacle avoidance and bottom-following operations in the vertical plane. Depth control and altitude control prevented the AUV from colliding with the sea bottom or with obstacles and prevented the AUV from diving beyond its maximum depth limit when bottom following. Simulation and laboratory trials with various seabed contours indicated that with the H∞ controller, the AUV was able to safely reach appointed destinations without collisions. Tests also showed that the H∞ controller was robust and suppressed interference, hence ensuring the precision of its navigation control. The proposed H∞ controller combined with the APFM has thus been proved to be both feasible and effective.

Single beacon passive positioning is crucial for the covert and rapid navigation of autonomous underwater vehicles (AUVs). Traditional methods, which assume a fixed beacon position, known sound speed, and error-free AUV heading, limit the applicability and accuracy of passive positioning systems. In this article, we proposed an angle-based passive positioning algorithm, underwater moving bearing angle of arrival (U-MBAOA), to solve the above problems. First, we replace the bottom-anchored fixed beacon with a mobile surface beacon and develop a dynamic positioning model to accommodate its movement. Then, to enhance the positioning accuracy, we parameterized the sound speed and derived a weighted least squares method to estimate the sound speed. Finally, we constructed an error model for heading errors and positioning under a straight-line trajectory and combined this model with U-MBAOA, achieving the estimation of heading errors in passive positioning for the first time. Through simulation analysis and sea tests, we confirmed that our algorithms outperform the existing algorithms.

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

The development of autonomous underwater vehicles (AUVs) for scientific, military and commercial purposes in applications such as ocean surveying, unexploded ordnance hunting and cable tracking and inspection requires the correspond ing development of navigation, guidance and control (NGC) systems, which should work in accord with each other for proper operation. Navigation systems are necessary to provide knowledge of vehicle position and attitude. The guidance systems manipulate the output of the navigation systems to generate suitable trajectories to be followed by the vehicle. This takes into account the target and any obstacles that may have been encountered during the course of a mission. The control systems are responsible for keeping the vehicle on course as specified by the guidance processor. In the Hammerhead AUV, this is achieved through manipulating the rudder and the hydroplanes (canards) of the vehicle. The need for accuracy in NGC systems is paramount. Erroneous position and attitude data in navigation systems can lead to a meaningless interpretation of the collected data, which in turn affects the accuracy of the corresponding guidance and control systems. This, if not contained properly may lead to a catastrophic failure of an AUV during a specific mission. The integrated NGC of the Hammerhead AUV is depicted.