Purpose of Review: The goal of this paper is to review current developments in the area of underwater robotics regarding the use of AI, especially in model learning, robot control, perception and navigation as well as manipulation.Recent Findings: AI technologies and advanced control techniques are finding their way into robotics systems to deal with complex and challenging conditions and to equip them with higher levels of autonomy.Summary: Although AI techniques and concepts are already a focus area in research on autonomous underwater systems, broad adoption to commercial systems is still in its infancy. Nonetheless, major advances have been done in recent years, especially on integrating different capabilities (perception, navigation, advanced control) in a single system and with first approaches on interaction and autonomous manipulation.

Continuum robots offer compliant and dexterous operations, which are suitable to be used in unstructured environments. Tendon-driven catheters, owing to their continuum structure, are applied in minimal invasive surgeries such as intracardiac interventions. However, due to the intrinsic nonlinearities and external disturbances, it is still a challenging task to accurately steer the catheter tip to the desired 3D positions. In this article, we proposed a new probabilistic kinematic model and a model-based three-dimensional position control scheme for a tendon-driven cardiac catheter. A dynamic Gaussian-based probabilistic model is developed to learn a mapping from the catheter states to the control actions. Based on the probabilistic model, a closed-loop position control is developed, in which the catheter is driven by a newly designed catheter driver system and tracked by a multiple near-infrared camera system. The proposed catheter framework is evaluated by the 3D trajectory tracking experiments both in a real 3D open space and in a minimum-energy-based simulator. The proposed control framework approximates the general kinematic by a combination of a catheter translation model and a distal workspace model, which provides the ability of automatically positioning the catheter tip in 3D and improving the accuracy by compensating the learned nonlinear effects.

This paper presents the design of the robot AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform to perform research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom, including 7-DOF arms, 4-DOF torso, 2-DOF head, and a mobile base equipped with six wheels, each of them with two degrees of freedom. The primary design goal was to achieve a lightweight arm construction with a payload-to-weight ratio greater than one. Besides, an adjustable body should sustain the dual-arm system providing an extended workspace. In addition, mobility is provided by means of a wheel-based mobile base. As a result, AILA's arms can lift 8kg and weigh 5.5kg, thus achieving a payload-to-weight ratio of 1.45. The paper will provide an overview of the design, especially in the mechatronics area, as well as of its realization, the sensors incorporated in the system, and its control software.

This paper describes data-driven modelling methods and their use for the control of a novel set of series-elastic actuators (SEAs). A set of elastic actuators was developed in order to fulfill the end-user needs for tailored industrial collaborative robot manipulators of different morphologies and payloads. Three different types of elastic actuation were investigated, namely, disc springs, coil springs and torsion bars. The developed algorithms were validated both on single actuators and on a 6-DOF robotic arm composed of such actuators.