Active Bathymetric SLAM for autonomous underwater exploration

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.

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.

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.

Precise navigation of autonomous underwater vehicles (AUV) is one of the most important challenges for marine applications. Current 2-dimensional (2D) navigation systems (algorithms) are effective to autonomous surface vehicles (ASV) operations, but still have some limitations for AUV navigation in 3D underwater environment. In this paper, a 3D range navigation algorithm is proposed that is potentially useful for small AUVs that have severe volume and power constraints. This algorithm enables an AUV to compute its trajectory in the presence of unknown currents and simultaneously estimate the currents by using range measurements from a single known location (beacon). The principal work here is the observability analysis of the proposed navigation system that characterizes observable AUV trajectories using novel approaches towards uniform observability of linear time varying systems. Then, implementation of the 3D range navigation algorithm as a discrete time extended Kalman filter is given based on the observability results and kinematic model of the system. Finally, a set of comparative simulation results verify the validity of the navigation algorithm and the system's observability related to trajectories.

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.

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.

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.

This paper proposes a novel active Simultaneous Localization and Mapping (SLAM) method with continuous trajectory optimization over a stochastic robot dynamics model. The problem is formalized as a stochastic optimal control over the continuous robot kinematic model to minimize a cost function that involves the covariance matrix of the landmark states. We tackle the problem by separately obtaining an open-loop control sequence subject to deterministic dynamics by iterative Covariance Regulation (iCR) and a closed-loop feedback control under stochastic robot and covariance dynamics by Linear Quadratic Regulator (LQR). The proposed optimization method captures the coupling between localization and mapping in predicting uncertainty evolution and synthesizes highly informative sensing trajectories. We demonstrate its performance in active landmark-based SLAM using relative-position measurements with a limited field of view.

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.

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.

As the modern utilization of the maritime environment only grows, uncrewed systems present the future of safety, efficiency, and capability. For submerged operations, Autonomous Underwater Vehicles (AUVs) enable scientists, industry, and militaries to access remote, inhospitable locations and execute a variety of tasks beyond the capabilities of human occupied or operated systems. Much of this autonomy relies on the vehicle having a detailed understanding of its own position. Inertial Navigation Systems provide an estimate of the distance traveled by combining numerous sensors, but are subject to unbounded error accumulation over long distances. Traditional methods of correcting for this error found in terrestrial robotics are largely unavailable in the undersea domain due to the absorption and scattering effects of electromagnetic signals in water. Acoustic communications and imaging such as Sound Navigation and Ranging (SONAR) is the most reliable and trusted method for AUVs. This thesis presents a novel method for performing Simultaneous Localization and Mapping (SLAM) through acoustic means utilizing a Mechanically Scanned Imaging Sonar (MSIS). MSIS utilize a single beam sonar mechanically rotated around the vehicle to scan a full 360◦ area. Compared with other sonar systems of similar capabilities, they require less size, weight, and power, and are available at a lower price point. The primary contribution of this thesis is a SLAM processing pipeline from MSIS to global position estimate. The pipeline extracts information from the MSIS data regarding the vehicle’s relative location compared to observed landmarks and then probabilistically matches the observed data to a best estimate vehicle position. The system is compatible with either an a priori map or a constantly updated SLAM global map. Individual beams from the MSIS are fused together into a submap. Contrast-based image processing identifies features of interest in the submap and appropriate features are then classified as observed landmarks. A probabilistic coarse-to-fine voting scheme identifies the most likely pose of the vehicle using the global map. When performing SLAM without an a priori map, observed landmarks are then evaluated and either added to the global map or used to update the position of known landmarks. While prior works have established MSIS SLAM by focusing on a single return per sonar beam, this thesis utilizes submaps to extract numerous features from a series of consecutive beams, allowing for more detailed and comprehensive feature mapping. Experimental validation was performed using an ISS360 sonar mounted on a REMUS-100 AUV with the processing pipeline running via Robot Operating System on the vehicle backseat computer. The vehicle was assisted by divers traversing underneath the WHOI Iselin pier and performed both localization and SLAM using the submerged pier pilings. The system performed real-time localization, successfully bounding previously unbounded localization drift to an average of 3.4m, resulting in over a 90% reduction in absolute error after approximately one hour of submerged operations. The SLAM results mirrored the a priori accuracy demonstrating similar error bounds validating the system performance.

This work addresses the implementation of the navigation, guidance and control of autonomous underwater vehicles on mobile acoustic networks. After a theoretical discussion of the algorithms required for such operation, we present results from simulation and real experiments that validate the proposed solutions.