Motion planning for an autonomous Underwater Vehicle via Sampling Based Model Predictive Control

Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.

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

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots’ joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments.

This paper introduces a quasi‐linear parameter‐varying model predictive control (qLPV MPC) framework for robust trajectory tracking of fully actuated unmanned underwater vehicles (UUVs), explicitly considering external disturbances and input constraints. To address the challenges of nonlinear dynamics, actuator limitations, and external disturbances, the UUV system is formulated as a qLPV model to balance the computational efficiency and the control accuracy. Then, a qLPV MPC method is designed for UUVs by incorporating an auxiliary feedback control law and a terminal constraint set, which ensures iterative feasibility and asymptotic stability. The input‐to‐state stability (ISS) is mathematically proven through Lyapunov‐based analysis, so that the proposed framework could stabilize bounded tracking errors despite system disturbances. Finally, an iterative solution method based on sequential quadratic programming (SQP) is introduced to obtain efficient solutions of the optimal control sequence while reducing the computational complexity. Simulation studies are conducted to validate the effectiveness of the proposed controller. The results show that the proposed method exhibits smaller tracking errors and faster convergence speeds compared to traditional robust nonlinear model predictive control (NMPC).

Recently, the autonomous underwater vehicle (AUV) has become a research hotspot with regard to unmanned underwater vehicles (UUVs). The AUV has been widely used in marine scientific research, marine resource investigations, and marine security assurance. With the support of the state, China has made an important breakthrough in deep-sea AUV technology. AUVs have played an irreplaceable role in various deep-sea resource surveys on a large scale in voyage applications. The paper introduces the research status of foreign AUVs, the development of Chinas deep-sea AUVs, and prospects the trend of the deep-sea AUV in China.

Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics

The advantages of using unmanned underwater vehicles in coastal ocean studies are emphasized. Two types of representative vehicles, remotely operated vehicle (ROV) and autonomous underwater vehicle (AUV) from University of South Florida, are discussed. Two individual modular sensor packages designed and tested for these platforms and field measurement results are also presented. The bottom classification and albedo package, BCAP, provides fast and accurate estimates of bottom albedos, along with other parameters such as in-water remotes sensing reflectance. The real-time ocean bottom optical topographer, ROBOT, reveals high-resolution 3-dimentional bottom topography for target identification. Field data and results from recent Coastal Benthic Optical Properties field campaign, 1999 and 2000, are presented. Advantages and limitations of these vehicles and applications of modular sensor packages are compared and discussed.

Vision-based motion planning of autonomous underwater vehicles (AUVs) is regarded as a critical requirement for marine intelligent transportation systems. However, the limited vision range and the uncertain model parameters of an AUV make it difficult to fulfill this requirement. This study focuses on a binocular-vision-based motion planning issue for an AUV. First, we develop an intelligent AUV system that mainly comprises binocular cameras for patrolling the target, a localization unit for acquiring the position information, and an acoustic modem for communicating with buoys. Accordingly, the parallax angles from the AUV to target are used to construct an optimal motion planning problem. To solve the aforementioned problem, we develop a deep reinforcement learning method called the improved twin delayed deep deterministic (TD3) policy gradient algorithm in order to minimize the reward function, such that the AUV can perpendicularly patrol the target with a fixed distance. The advantages of our solution are as follows: 1) the binocular-vision-based motion planning method can achieve a trade-off between motion stability and observation effectiveness; 2) the improved TD3 algorithm can accelerate the convergence compared to other algorithms, while it can simultaneously overcome the dependency on the model parameters of the AUV. Finally, simulation and experimental studies are conducted to verify the effectiveness.

Unmanned Underwater Vehicles (UUVs) are pivotal for underwater exploration and maintenance. Autonomous Underwater Vehicles (AUVs), with their potential to reduce operational time and environmental impact, are gaining increased interest. However, they face important technological challenges, particularly in power supply. This study focuses into the application of Inductive Wireless Power Transfer (IWPT) for continuous AUV operation, employing tightly coupled split core transformers (SCT) designed for near-field power transfer. Robust isolation and alignment mechanisms are proposed to overcome the effects of seawater environment. An IWPT device with an SCT and resonant LLC circuit is simulated and experimentally tested. Finite Element Method studies highlight the advantage of isolating the device from the seawater environment, especially at high frequencies. LLC simulation and experimental results demonstrate an efficiency in power transfer of 93.2 % and 87.1 %, respectively, transferring up to 312 W. However, global efficiency of the experimental device drops to 76.4 %, highlighting the need for optimizations in circuit design. Keywords. Inductive Wireless Power Transfer, Autonomous Underwater Vehicles, Resonant Converters, Finite Element Analysis.

Due to their utility in replacing workers in tasks unsuitable for humans, unmanned underwater vehicles (UUVs) have become increasingly common tools in the fish farming industry. However, earlier studies and anecdotal evidence from farmers imply that farmed fish tend to move away from and avoid intrusive objects such as vehicles that are deployed and operated inside net pens. Such responses could imply a discomfort associated with the intrusive objects, which, in turn, can lead to stress and impaired welfare in the fish. To prevent this, vehicles and their control systems should be designed to automatically adjust operations when they perceive that they are repelling the fish. A necessary first step in this direction is to develop on-vehicle observation systems for assessing object/vehicle–fish distances in real-time settings that can provide inputs to the control algorithms. Due to their small size and low weight, modern cameras are ideal for this purpose. Moreover, the ongoing rapid developments within deep learning methods are enabling the use of increasingly sophisticated methods for analyzing footage from cameras. To explore this potential, we developed three new pipelines for the automated assessment of fish–camera distances in video and images. These methods were complemented using a recently published method, yielding four pipelines in total, namely, SegmentDepth, BBoxDepth, and SuperGlue that were based on stereo-vision and DepthAnything that was monocular. The overall performance was investigated using field data by comparing the fish–object distances obtained from the methods with those measured using a sonar. The four methods were then benchmarked by comparing the number of objects detected and the quality and overall accuracy of the stereo matches (only stereo-based methods). SegmentDepth, DepthAnything, and SuperGlue performed well in comparison with the sonar data, yielding mean absolute errors (MAE) of 0.205 m (95% CI: 0.050–0.360), 0.412 m (95% CI: 0.148–0.676), and 0.187 m (95% CI: 0.073–0.300), respectively, and were integrated into the Robot Operating System (ROS2) framework to enable real-time application in fish behavior identification and the control of robotic vehicles such as UUVs.

Autonomous Underwater Vehicles (AUVs) are Unmanned Underwater Vehicles (UUVs) that are able to function without direct control from a human operator. Consequently, they have a wide range of applications from scientific research of the oceans to military applications such as maritime surveillance. However, there is now the demand for AUVs to be operated within a multi-vehicle scenario to allow large areas of the ocean to be monitored simultaneously. However, in order for this to become a reality algorithms have to be created that ensure that a group of AUVs could be self-organising. Therefore, using a validated mathematical model of a biomimetic robotic fish (called RoboSalmon) and taking inspiration from nature, this paper outlines the implementation of co-ordination algorithms based upon the behavioural mechanisms exhibited by schools of fish to allow a group of AUVs to become self-organising. The algorithms implemented are based on two different methodologies known as the Discrete and Continuous Behavioral Zone methodologies. The results obtained demonstrated that although both methodologies result in the formation of a school structure, the results obtained from the Continuous Behavioral Zone (CBZ) methodology were more resilient to changes in parameters associated with school structures and therefore these algorithms provided the most effective way to allow a group of AUVs to be considered as self-organising.

Unmanned Underwater Vehicle (UUV), known as underwater drones, are any vehicle that are able to operate underwater without human occupant. AUV (Autonomous Underwater Vehicle) are one of categories of these vehicles which operate independently of direct human input. This AUV is required to have a navigation system that can manoeuvred 6 Degree of Freedom (DOF) and able to estimate the exact position based on the determined trajectory. Fuzzy Kalman Filter (FKF) method is used to estimate the position of the AUV. This process is used to maintain the accuracy of the trajectory. The performance of FKF algorithm on some several trajectory cases show that this method has relatively small Root Means Square Error (RSME), which is less than 10%.

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies.

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

: The long-term goal of this research is to provide a capability to extend limited observations of local waves and currents into a larger domain using physics-based models within the littoral zone. It is anticipated that unmanned underwater vehicles (UUVs) and/or underwater autonomous vehicles (UAVs) will be used to provide local observations, and the tool will be able to be run quickly on a laptop. The objectives of the proposed program are to utilize video from UAVs in the littoral region to generate estimates of wave conditions and bathymetry. This would be the first step to more long-term goals, which would require incorporating UUV and in situ observations as well as current estimations. The proposed program will have three parts: (1) develop a ray-trace solver for the wave action balance equation that will allow the prediction of shoaling wave spectra given the local bathymetry and currents, and the assimilation of wave observations inside the domain into the wave field coming into the domain that would reproduce the observations; (2) using the ray-trace solver, generate estimates of wave conditions and bathymetry from video observations of breaking waves; and (3) using the ray-trace solver, generate estimates of wave conditions from video observations of nonbreaking waves (i.e. seaward of the surf zone). So far, the authors have developed the ray-trace solver to the wave action balance equation for shoaling waves and incorporated the sources and sink terms that are in the SWAN model. This required some modifications to the SWAN approach. They have done a comparison between SWAN and this model for a linear beach and a quasi-monochromatic wave and have shown that they are identical (i.e., they can reproduce the SWAN result). They are currently performing a comparison between SWAN and this model for a more realistic beach (the actual bathymetry measured at Duck, NC) and a more realistic wave spectrum (a Pierson-Moskowitz wind-wave spectrum).