The Underwater Swimming Manipulator—A Bioinspired Solution for Subsea Operations

Abstract

Underwater vehicles have carried out subsea operations for many decades, and since the 1980s, remotely operated vehicles (ROV) 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 (I-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 modelling, 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 a 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

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

“…where T (q) ∈ R 6×m is the thruster configuration matrix, τ thr ∈ R m is the vector of thruster forces, and τ q ∈ R (n−1) represents the joint torques. To implement the control input, a thruster allocation scheme as proposed in [27] needs to be implemented to distribute the desired control inputs onto the thrusters. The vector of body-fixed velocities, ζ, is defined as…”

Tracking control of an articulated intervention AUV in 6DOF using the generalized super-twisting algorithm

“…where T (q) ∈ R 6×m is the thruster configuration matrix, τ thr ∈ R m is the vector of thruster forces and τ q ∈ R (n−1) represents the joint torques. To implement the control input τ (q), a thruster allocation scheme as proposed in [26] needs to be implemented to distribute the desired control inputs onto the m thrusters. The vector of body-fixed velocities, ζ, is defined as…”

“…Theorem 1: Consider the error dynamics given by (13) and let the sliding surface σ be defined by (15). Let the control input be given by (26). Then, the closed-loop dynamics is described by (28), and the origin of this cascade system is uniformly globally asymptotically stable (UGAS), which ensures the asymptotic convergence of the tracking error when…”

“…II. In the simulation we use an inverse kinematic controller to give us the reference that we want the AIAUV to follow, as proposed in [26]. The thruster allocation matrix is also implemented as proposed in [26].…”

Underwater Robotic Arm Test Platform

“…The performance of the motor also directly determines the control accuracy of the arm. Therefore, it is important to select a suitable motor [12]. Through the analysis of the requirements of the mechanical arm test performance to be satisfied by the underwater manipulator test platform, the selection of the motor needs to be satisfied, the high torque supports load, high precision, and the rotation and stop.…”