Intervention Autonomous Underwater Vehicle Research Articles

Power-Based Safety Constraint for Redundant Robotic Manipulators

A robot making or losing contact with its environment will experience a sudden change in its dynamics. This may cause instability, possibly causing the robot to harm itself and its environment. To prevent this while not placing overly restrictive constraints on the energy generated by the controller, we place a time-varying constraint on the system's power. The power limit varies with a heuristic measure of a desired task trajectory's stability, which is based on the largest Lyapunov exponent. When the trajectory is deemed unstable, the controller is forced to dissipate energy, while it is allowed to generate energy when the trajectory is stable. The constraint is included in a strict task-priority framework, allowing a redundant robotic platform to perform several tasks simultaneously while ensuring that the performance of the higher-priority tasks is not affected by the lower-priority tasks. The presented method is validated by simulation of an articulated intervention autonomous underwater vehicle (AIAUV).

Inspection and maintenance of industrial infrastructure with autonomous underwater robots.

Underwater infrastructure, such as pipelines, requires regular inspection and maintenance including cleaning, welding of defects and valve-turning or hot-stabbing. At the moment, these tasks are mostly performed by divers and Remotely Operated Vehicles (ROVs) but the use of intervention Autonomous Underwater Vehicles (intervention-AUVs) can greatly reduce operation time, risk, and cost. However, autonomous underwater manipulation has not yet reached a high technological readiness and is an intensively researched topic. This review identifies key requirements based on necessary inspection and maintenance methods, linking them to the current technology and deriving major challenges which need to be addressed in development. These include the handling of tools, where a separation between handheld and mounted tools is detected in already employed underwater intervention vehicles such as the Sabertooth by Saab Seaeye or the Aquanaut by Nauticus robotics, two vehicles capable of semi-autonomous intervention. The main challenge identified concerns high level autonomy, i.e., the process of decision-making. This process includes detecting the correct point of interest, maximizing the workspace of the manipulator, planning the manipulation considering required forces, and monitoring the progress to allow for corrections and high quality results. In order to overcome these issues, reliable close range sensing and precise end point navigation is needed. By identifying these persisting challenges, the paper provides inspiration for further development directions in the field of autonomous underwater intervention.

Robust Hierarchical Tracking Control of Vehicle-Manipulator Systems

For vehicle-manipulator systems (VMSs) to perform precise operations, a robust tracking control framework is required. Moreover, to exploit the platform's redundancy, it is desirable that the framework allows several tasks to be completed simultaneously. This work presents a robust hierarchical tracking controller for floating-base robots. In addition to providing uniform global asymptotic stability (UGAS) in free motion and allowing a total task dimension greater than the number of degrees of freedom (DoFs), the proposed controller includes a sliding mode effect and is proved to achieve UGAS even in the presence of bounded disturbances. Moreover, it is proved that uniform global ultimate boundedness (UGUB) is obtained if a continuous approximation of the sliding mode term is used. The stability results are proved mathematically, and are validated through simulations of an articulated intervention autonomous underwater vehicle (AIAUV).

Design and Implementation of Lightweight AUV With Multisensor Aided for Underwater Intervention Tasks

Autonomous underwater vehicles (AUVs) are commonly used to conduct complex underwater tasks, such as marine infrastructure overhaul and maintenance, environmental monitoring, oceanographic mapping, and organism capture. These tasks require the ability of an AUV to perform autonomous navigation, especially when communication is limited in the underwater environment. This brief developed a new type of lightweight intervention AUV for autonomous navigation using data from multiple inertial sensors, where multi-sensor error state Kalman filter schemes are preferable to standard Kalman Filters in terms of the AUV’s motion estimates. Concerning target recognition, a color restoration method is provided for degraded underwater images and a You Only Look Once strategy is combined with topological analysis for object detection. In addition, the proposed design is robust in terms of its software components and mechanical structure, which provides a feasible platform for AUV’s secondary development. Experiments of surveying and object manipulation conducted in underwater environments demonstrate the functionality of the entire system and its potential applications in the fields of science and industry.

Trajectory tracking of Intervention-Autonomous Underwater Vehicle via a robust adaptive finite-time control

The trajectory tracking of Intervention-Autonomous Underwater Vehicle (I-AUV) under hydrodynamic, uncertainties, disturbances and dynamic interaction is studied in this paper. A robust adaptive finite-time control is proposed to attain the high tracking accuracy and strong robustness. Firstly, to enhance the dynamical performance, a continuous terminal sliding mode surface is designed to guarantee that surface and errors are stable finite-time convergent to origin. Second, a feedback control term is developed to depict the hydrodynamic, uncertainties and disturbance estimation error. Then, an adaptive nested estimation technology is developed to approach the disturbance, in which a virtual torque is tuned by the low-pass filtering, and dual nested adaptive laws are provided to approximate this virtual torque and its time derivative, which can constrain the residual error to a small elliptic domain. The validity of our proposed control is verified by Lyapunov stability theory, and its reliability is proved in the simulation cases.

The generalized super-twisting algorithm with adaptive gains.

In this article, a novel adaptive generalized super‐twisting algorithm (GSTA) is proposed for a class of systems whose perturbations and uncertain control coefficients may depend on both time and state. The proposed approach uses dynamically adapted control gains, and it is proven that this ensures global finite‐time convergence. A nonsmooth strict Lyapunov function is used to obtain the conditions for global finite‐time stability. A simulation and experimental case study is performed using an articulated intervention autonomous underwater vehicle (AIAUV). It is also shown that the adaptive GSTA causes the tracking errors of the AIAUV to converge to zero in finite time. In the case study, we use the singularity‐robust multiple task‐priority method to create a continuous trajectory for the AIAUV to follow. The simulation and experimental results validate and verify that the proposed approach is well suited for controlling an AIAUV. We also perform a comparison with the super‐twisting algorithm with adaptive gains and the original GSTA to evaluate whether adding adaptive gains to the GSTA actually improves the tracking capabilities by combining the theoretical advantages afforded by the GSTA with the practical advantages afforded by adaptive gains. Based on this comparison, the adaptive GSTA yields the best tracking results overall without increasing the energy consumption, and the simulations and experiments thus indicate that adding adaptive gains to the GSTA does indeed improve the consequent tracking results and capabilities.

Modular modeling and coordination control scheme for an underwater cooperative transportation performed by two I-AUVs

Intervention Autonomous Underwater Vehicles (I-AUVs), also known as Underwater Vehicle Manipulator Systems (UVMSs), are an alternative for reducing costs and increasing safety in underwater operations which require dexterous manipulation. Beyond the well-posed and currently studied manipulation scenarios, the cooperative underwater transportation is a promising and challenging task, still not extensively explored. This paper aims to contribute to this latter problem by addressing the modeling and control system design of a cooperative underwater transportation performed by two planar I-AUVs, each of them composed by an AUV linked to a 2-Degrees of Freedom (DoFs) serial manipulator. A hierarchical approach for deriving the equations of motion through the Modular Modeling Methodology (MMM) is presented. Also, a complete coordination scheme composed of the Inverse Kinematics (IK) and motion control modules is proposed. A new IK control is formulated on the jerk level with the Super-Twisting Algorithm (STA) to prevent numerical drift and uncertainties while considering secondary objectives. The low-level motion control is synthesized within a continuous Sliding Mode Control (SMC) algorithm to compensate for the modeling errors and external disturbances. The coordination scheme is verified through numerical simulation considering a scenario in which an object is transported by the two I-AUVs over a predefined path. The analysis is made more realistic by considering the thrusters and motors dynamics along with sensors measurements, sampling effects and an optimal state estimator. Modeling errors and external disturbances are also considered in the analysis. The results reveal the suitability of the proposed modeling and coordination approaches for numerical simulation and control of the I-AUVs during underwater transportation.

Comparison of two second-order sliding mode control algorithms for an articulated intervention AUV: Theory and experimental results

An articulated intervention autonomous underwater vehicle (AIAUV) is a slender, multi-articulated underwater robot. Accurate trajectory tracking is essential for AIAUV operations. Furthermore, due to hydrodynamic and hydrostatic parameter uncertainties, uncertain thruster characteristics, unknown disturbances, and unmodelled dynamic effects, robustness is crucial. In this paper, we present a super-twisting algorithm (STA) with adaptive gains and a generalized super-twisting algorithm (GSTA) for trajectory tracking of the position and orientation of AIAUVs. A higher-order sliding mode observer (HOSMO) for estimating the linear and angular velocities when velocity measurements are unavailable is also presented. The tracking errors for the resulting system are proven to converge asymptotically to zero. Finally, we demonstrate the applicability of the presented control laws with comprehensive simulation and experimental results and perform a comparison study, with two tests (C-shape and C-shape with a moving head), between the two algorithms and also a benchmark PID controller. The STA with adaptive gains exhibits the best overall tracking performance, with average position root mean square error (RMSE) 0.0121 m and average orientation RMSE 0.0335 rad. The GSTA also presented good results with average position RMSE 0.0267 m and average orientation RMSE 0.0292 rad. The PID controller gave average position RMSE 0.0371 m and average orientation RMSE 0.0491 rad.

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots' end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles' limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.

Tracking Control of an Articulated Intervention Autonomous Underwater Vehicle in 6DOF Using Generalized Super-twisting: Theory and Experiments

The articulated intervention autonomous underwater vehicle (AIAUV) is an underwater swimming manipulator with intervention capabilities. Station-keeping and trajectory tracking are essential for the AIAUV to be able to move in confined spaces and to perform intervention tasks. In this article, we propose using a generalized super-twisting algorithm (GSTA), which is an extension of the regular super-twisting algorithm, for the trajectory tracking of the position and orientation of the base of the AIAUV in six degrees of freedom (6DOF). We also propose using a higher-order sliding mode observer (HOSMO) for estimating the linear and angular velocities when velocity measurements are unavailable. Furthermore, we show the ultimate boundedness of the tracking errors for a control law using the GSTA and for a control law that combines the GSTA with an HOSMO. We also prove that the GSTA gives global uniform finite-time stability. Finally, we demonstrate the applicability of the proposed control laws with comprehensive simulation and experimental results.

Multirepresentation, Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment

AbstractA key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I‐AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I‐AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real‐time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR‐MHA*) search‐based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I‐AUV performing a Valve‐Turning intervention in a water tank. To this aim, the AUV was upgraded with an in‐house‐developed laser scanner to gather three‐dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR‐MHA* motion planner used this octomap to plan, in real time, collision‐free trajectories. To achieve the accuracy required to complete the task, a vision‐based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual‐servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose‐coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.

Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces

Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given planning problem. These heterogeneous structures are obtained via a Dirichlet process (DP) mixture model and together cover the entire configuration space, resulting in more dimensionality reduction than single-structure approaches from the existing literature. Then, a unified low-dimensional trajectory optimization problem is formulated based on the obtained heterogeneous structures and a proposed transversality condition which is further solved via SQP in our implementation. The positive results demonstrate the feasibility and efficiency of our trajectory planning approach on an autonomous underwater vehicle (AUV) and a high-dimensional intervention autonomous underwater vehicle (I-AUV) in cluttered 3D environments.

Task-Priority Control of Redundant Robotic Systems using Control Lyapunov and Control Barrier Function based Quadratic Programs

This paper presents a novel task-priority control framework for redundant robotic systems based on a hierarchy of control Lyapunov function (CLF) and control barrier function (CBF) based quadratic programs (QPs). The proposed method guarantees strict priority among different groups of tasks such as safety-related, operational and optimization tasks. Moreover, a soft priority measure in the form of penalty parameters can be employed to prioritize tasks at the same priority level. As opposed to kinematic control schemes, the proposed framework is a holistic approach to control of redundant robotic systems, which solves the redundancy resolution, dynamic control and control allocation problems simultaneously. Numerical simulations of a hyper-redundant articulated intervention autonomous underwater vehicle (AIAUV) is presented to validate the proposed framework.

Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation

Position/force control of an Intervention Autonomous Underwater Vehicle (I-AUV) is essential to many underwater intervention tasks (e.g., marine equipment maintenance, underwater welding and so on), and is challenging due to unknown fluid disturbances and model uncertainties. This paper applies the Sliding Mode Impedance Control (SMIC) to the full contact intervention of an I-AUV, from non-contact phase to contact phase. Both computational simulations and practical experiments are conducted to investigate the performance of SMIC. In simulations, considering model uncertainties and detailed fluid disturbances, accurate position and force tracking can be achieved, with the position tracking errors within ±3 × 10−3m and a Root Mean Square Error (RMSE) of 4 × 10−2N in tracking the desired contact force of 10N. For the purpose of experimental validation, the SMIC is implemented on an I-AUV developed in the University of Technology Sydney (UTS). The experimental results demonstrate the SMIC’s good performance in the contact intervention of the I-AUV, with the position tracking errors within ±1.6×10−2 m and a RMSE of 0.97N in maintaining the desired contact force of 10N.

Practical formulation of obstacle avoidance in the Task-Priority framework for use in robotic inspection and intervention scenarios

This paper presents a new formulation of a reactive obstacle avoidance algorithm, in the Task-Priority framework, delivering a practical solution for obstacle avoidance between vehicle-manipulator systems and complex environments. The presented concepts were implemented on an intervention autonomous underwater vehicle (I-AUV) and tested in an underwater pipe structure inspection and valve turning scenario, in a test tank, using GIRONA500 with an ECA 5E Micro manipulator. The obstacle avoidance is treated as an inequality (set-based) task, which takes into account all obstacles that are interacting with the robot links. Both the robot and the obstacles are represented by spheres to allow for analytical formulation. However, the environment is wrapped with spheres based on its actual geometry stored as an Octomap, hence it can be represented at different resolutions. Depending on the type of the mission performed by the robot we defined two modes of operation: (1) Navigation and Inspection, and (2) Intervention. For each mode, the algorithm takes into account different number of key points at the I-AUV and a different resolution of the environment representation. Typically, for the Intervention mode the resolution is higher, to allow for more precise motion. We also present an escape point strategy in case of the robot getting stuck between obstacles.

Investigation on the mechanical design and manipulation hydrodynamics for a small sized, single body and streamlined I-AUV

Operations with Intervention Autonomous Underwater Vehicle (I-AUV) can not only realize automatic and accurate missions, but also save the pilots’ endeavors and reduce their fatigues. However, the performance of a small sized I-AUV is easily affected by its own restoring forces and hydrodynamic effects. A novel small sized single body and streamlined I-AUV has been developed for autonomous cruising and manipulation through the schemes comparisons and analysis on the restoring forces and hydrodynamic performance. In order to investigate I-AUV manipulation hydrodynamic performance during manipulation, water channel experiments and computation fluid dynamics (CFD) have been made on various operation postures. Moreover, CFD simulations and analysis have been expanded from water channel domain to infinite domain to obtain I-AUV manipulation hydrodynamic performance during forward cruising.

Underwater Robots: From Remotely Operated Vehicles to Intervention-Autonomous Underwater Vehicles

Remotely operated vehicles (ROVs) revolutionized the subsea industry when they were introduced in the 1960s. They were more powerful and inherently safer, and they could go deeper than divers. However, they require a tether and a support ship, which makes their use complex and expensive. Autonomous marine vehicles (AMVs) have long been seen as a game changer in the exploration and exploitation of the marine environment. Repeated access to remote and hazardous places for data gathering and intervention, enabled by their autonomy, is the key to their adoption. While historically the focus has been on autonomous underwater vehicles (AUVs), unmanned surface vehicles (USVs) have recently been developed and adopted at an increasing rate. Interestingly, whereas AMVs (AUVs and USVs) have now been adopted in niche areas (bathymetric surveys and mine countermeasures), they have not yet hit the mainstream. In this article, we review the state of the art in unmanned underwater vehicles (UUVs), which include ROVs and AUVs; current obstacles to their adoption, both technical and commercial; and recent advances in technology. We also present an outlook on the future of these systems.

Multi-Representation Multi-Heuristic A* Motion Planning for a Dual-Arm Underwater Vehicle Manipulation System

Recently, Intervention-Autonomous Underwater Vehicles (I-AUVs), started to grab researchers attention. While prior systems rely on variations of the task-priority redundancy control framework, our recent research demonstrated experimental results using search-based motion planning for a single arm floating-based intervention in known and unknown environments. In this work, we present a search-based motion planning algorithm applied to the coordinated manipulation problem of a dual-arm Intervention AUV, in an object transportation mission (e.g. pipe transportation). Two Challenges are considered: the high-dimensionality of the system, and the motion coordination between the mobile base and both working arms. The latter challenge is a major one if accurate execution is required, especially considering the floating nature of the AUV. Our approach relies on exploiting the loose coupling between the different system components. We show for the first time the use of a search-based planner on a dual-arm underwater manipulator. In addition, we support our claims with a simulated demonstration, successfully performing a coordinated dual-arm underwater intervention.

Path following control for articulated intervention-AUVs using geometric control of reduced attitude

An articulated intervention autonomous underwater vehicle (AIAUV) consists of the jointed body of a snake robot equipped with thrusters for hovering and faster propulsion. The slender, articulated body can be used as a floating manipulator arm for subsea intervention. In order to extend the reach and operation time of the AIAUV, energy-efficient methods for longdistance travel are needed. This paper proposes a path-following control method for AIAUVs moving in 3D which reduces the use of thrusters by using them only for forward propulsion. The direction of travel is controlled by curving the body using its joints. As a first step, a control law for tracking of a reduced attitude is developed for a general system with two control torques, and shown to give asymptotic tracking of a time-varying reference reduced attitude. This control law is then used indirectly to provide references for the joint angles of an AIAUV, in order to control its pointing direction. By combining this with a line-of-sight (LOS) guidance law, a method for following straight paths is obtained. Simulation studies show that the method succeeds in making an AIAUV converge to and follow the desired path.

The Underwater Swimming Manipulator—A Bioinspired Solution for Subsea Operations

Underwater vehicles have carried out subsea operations for many decades, and since the 1980s, remotely operated vehicles (ROVs) have been essential for the development and maintenance of subsea installations. As the technology has progressed, various types of vehicles have been developed to perform subsea inspection, maintenance, and repair (IMR) operations, including conventional work class ROVs, inspection class ROVs, autonomous underwater vehicles (AUVs), and, more recently, intervention AUVs. The underwater swimming manipulator (USM), presented in this paper, is an innovative bioinspired addition to the family of underwater robotic vehicles. The overall vision of the USM is to provide a significant impact on how to perform inspection and light intervention tasks. In this paper, we discuss the most important applications for the USM and the main challenges related to modeling, guidance, and control of this innovative vehicle. We provide a detailed description of the concept of the USM, together with a proposed generic motion control framework. A kinematic and dynamic model of the USM is derived for the purpose of designing control algorithms, and selected task-based control approaches are presented, based on inverse kinematic control. We also present the development of a simulation environment, a simulation model of the USM, and provide simulations to support the use of USMs for subsea IMR operations.