Terrain‐aided navigation for long‐range AUVs in dynamic under‐mapped environments

This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

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.

Research on position correction method for AUV large depth navigation based on ranging positioning

In recent years, terrain-aided navigation (TAN) has been rapidly developed for the localization of autonomous underwater vehicles (AUVs). TAN provides a position estimate for vehicles relative to a priori topographic map and is typically a nonlinear filtering model. Particle filtering has attracted considerable attention for addressing nonlinear and non-Gaussian challenges. However, it has problems with particle degradation and particle impoverishment. To tackle the problem in this study, the particle swarm optimization (PSO) optimization approach is employed to increase particle variety while driving particles towards the high probability zone through iterative updates. In addition, prior information is introduced to the particle fitness to reduce the interference of pseudo-peaks and increase the estimate precision. Finally, simulation tests are implemented to verify the effectiveness of the proposed method. Simulation results indicate that the improved GPSO-PF method performs better in the case of likelihood function with multiple peaks.

SUMMARY Dedicated research vessels that are capable to operate during all seasons of the year and under unfavourable weather conditions in the central Arctic Ocean and in the Southern Ocean are required to fulfil the new needs of polar ocean research for all marine disciplines. Today, no available research vessel has these required capabilities. Thus, the initiative was taken to develop a novel and dedicated research icebreaker with technical capabilities hitherto unrealised, which will enable the vessel to autonomously operate in the Arctic Ocean even during the severest ice conditions in the deep winter serving all marine disciplines of polar research including deep-sea drilling: The AURORA BOREALIS. The technical conceptual design of this research icebreaker with scientific deepsea drilling capability AURORA BOREALIS is summarised in this report. AURORA BOREALIS is planned to be a „European scientific flagship facility‟ open to non-European partners, a multidisciplinary platform for studies ranging from the sub-seafloor into the atmosphere. The ability of AURORA BOREALIS to penetrate into the harshest conditions on Earth and to carry out research in the polar winter can set new standards in the fields of polar research and naval architecture, including environmental safety and sustainability of the highest standards. Currently, no polar research vessel has the capability to autonomously operate in pack ice outside the optimal ice conditions of the late summer season. AURORA BOREALIS, in contrast, is planned as a multi-purpose icebreaking research vessel for Arctic and Antarctic operations with the capability to autonomously navigate in sea-ice with a thickness of more than 2.5 metres. This will for the first time facilitate year-round research, e.g. on the nature of global environmental change. The ship shall have the unique capability to perform scientific deep-sea drilling operations in water depths between 100 and 5000 metres with a penetration of more than 1000 metres into the seafloor, even while being located amid drifting pack-ice fields. To perform these drilling operations AURORA BOREALIS has to be kept exactly on position. A dynamic positioning system capable for manoeuvring and staying on position in drifting sea-ice is mandatory for this task – an absolute novelty in the shipping industry. Another unique characteristic of AURORA BOREALIS are the two moon pools (7 x 7 meters each) in the midst of the hull that reach into the water below the vessel and enable scientists to deploy equipment into the ocean without being subject to wind, waves and ice. The aft moon pool is mainly dedicated to drilling operations, while the forward moon pool is reserved for most other scientific equipment. This allows as a first the deployment of very sensitive and expensive equipment, e.g. remotely operated (ROVʼs) or autonomous (AUVʼs) underwater vehicles within completely closed sea ice cover. Scientific laboratories are located on several decks around the moon pool, which is designed in an atrium-like shape with circular walkways and preparation areas. In order to optimally equip the ship even for mission specific expeditions, containerized laboratories can be also loaded here and become fully integrated into the scientific workflow on board.

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (<5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass are often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous underwater vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here, we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (<5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photographs of the seagrass were collected along transects via snorkelling or an AUV. Photographs from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object-based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller-collected field data-sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison with snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5% of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programmes.

This paper focuses on mobile positioning for autonomous underwater vehicle (AUV) in complex underwater environments. The positioning algorithm based on time of arrival (TOA) assumes that the underwater acoustic communication between AUV and underwater sensors is real-time and reliable. The distance between the AUV and underwater sensors can be obtained through the time delay of acoustic signal transmission. However, the uncertain and complex nature of the underwater environment can cause underwater sensors to fail to receive acoustic signals from the AUV reliably. This leads to an insufficient effective observation data in the positioning solution process, causing underdetermined problems in the solution equation, and thus making it impossible to solve the AUV’s position information stably in real time. To overcome this problem, we frist established a positioning model considering the navigation velocity of the AUV. Based on this Model, we then proposed a two-step weighted least squares algorithm combined with a fast horizontal distance model for the deep sea (TWLS-HD-V). Furthermore, we derived the Cram´er-Rao Lower Bound (CRLB) of the proposed model and compared it with that of the basic model. Finally, the effectiveness of the proposed algorithm was tested on both numerical simulations and deep-sea data. In conclusion, this algorithm effectively overcomes two key challenges in complex underwater environments: sound line bending and insufficient time delay information. As a result, it improves AUV positioning accuracy and ensures reliable positioning trajectories.

Towards Arctic AUV Navigation

Summary The current stage is characterized by high activity of the world community in the field of research of deep-water oil and gas basins and new promising types of solid minerals. A promising direction in terms of improving the efficiency and reliability of exploration results on hydrocarbon and solid mineralsof the world ocean is the use of Autonomous unmanned underwater vehicles, which allow for detailed large-scale bottom surveys, quickly and efficiently. The required component of the Autonomous underwater complex is the means of measuring the components of the magnetic and gravitational fields. The report provides an overview of modern foreign and domestic technologies in the field of underwater deep-sea gravimetric and magnetometric surveys, analyzes the results of such works and formulates a range of exploration tasks that are promising for their application.

Improved monitoring of HABs using autonomous underwater vehicles (AUV)

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.