Autonomous Grasping Using Novel Distance Estimator

This paper introduces a novel distance estimator using monocular vision for autonomous underwater grasping. The presented method is also applicable to topside grasping operations. The estimator is developed for robot manipulators with a monocular camera placed near the gripper. The fact that the camera is attached near the gripper makes it possible to design a method for capturing images from different positions, as the relative position change can be measured. The presented system can estimate relative distance to an object of unknown size with good precision. The manipulator applied in the presented work is the SeaArm-2, a fully electric underwater small modular manipulator. The manipulator is unique in its integrated monocular camera in the end-effector module, and its design facilitates the use of different end-effector tools. The camera is used for supervision, object detection, and tracking. The distance estimator was validated in a laboratory setting through autonomous grasping experiments. The manipulator was able to search for and find, estimate the relative distance of, grasp, and retrieve the relevant object in 12 out of 12 trials.publishedVersio

Autonomous subsea intervention (SEAVENTION)

This paper presents the main results and latest developments in a 4-year project called autonomous subsea intervention (SEAVENTION). In the project we have developed new methods for autonomous inspection, maintenance and repair (IMR) in subsea oil and gas operations with Unmanned Underwater Vehicles (UUVs). The results are also relevant for offshore wind, aquaculture and other industries. We discuss the trends and status for UUV-based IMR in the oil and gas industry and provide an overview of the state of the art in intervention with UUVs. We also present a 3-level taxonomy for UUV autonomy: mission-level, task-level and vehicle-level. To achieve robust 6D underwater pose estimation of objects for UUV intervention, we have developed marker-less approaches with input from 2D and 3D cameras, as well as marker-based approaches with associated uncertainty. We have carried out experiments with varying turbidity to evaluate full 6D pose estimates in challenging conditions. We have also devised a sensor autocalibration method for UUV localization. For intervention, we have developed methods for autonomous underwater grasping and a novel vision-based distance estimator. For high-level task planning, we have evaluated two frameworks for automated planning and acting (AI planning). We have implemented AI planning for subsea inspection scenarios which have been analyzed and formulated in collaboration with the industry partners. One of the frameworks, called T-REX demonstrates a reactive behavior to the dynamic and potentially uncertain nature of subsea operations. We have also presented an architecture for comparing and choosing between mission plans when new mission goals are introduced.publishedVersio

Monocular vision-based gripping of objects

Optics-based systems may provide high spatial and temporal resolution for close range object detection in underwater environments. By using a monocular camera on a low cost underwater vehicle manipulator system, objects can be tracked by the vehicle and handled by the manipulator. In this paper, a monocular camera is used to detect an object of interest through object detection. Spatial features of the object are extracted, and a dynamic positioning system is designed for the underwater vehicle in order for it to maintain a desired position relative to the object. A manipulator mounted under the vehicle is used to retrieve the object through a developed kinematic control system. Experimental tests verify the proposed methodology. A stability analysis proves asymptotic stability properties for the chosen sliding mode controller and exponential stability for the task error

Experimental Validation of End-Effector Stabilization for Underwater Vehicle-Manipulator Systems in Subsea Operations

This paper considers the kinematic control approach for controlling an underwater vehicle-manipulator system. Three different kinematic control schemes have been applied, and the performance of each scheme is compared. The kinematic control schemes provide velocity references, while the control system aims to keep a fixed position for the manipulator’s end-effector, and at the same time compensate for slowly varying motions of the underwater vehicle. Experimental results show that the proposed full modified kinematic control scheme has better performance than the decoupled kinematic control scheme, while it nicely outperforms the full kinematic control scheme. All the control schemes are good alternatives for controlling an underwater vehicle-manipulator system using kinematic control

Probabilistic Localization and Mapping of Flexible Underwater Structures using Octomap

This paper addresses localization and mapping of flexible ocean structures. The methodology is based on a network of low cost acoustic transmitters on the structure and receivers stationed in the vicinity of the structure. The position of the receivers is assumed to be known. The position of each transmitter is estimated and represents a point position on the structure. All of the point positions are interpolated and applied in the mapping software Octomap, to build a digital map representation of the whole flexible structure. This map can serve as an environment model for underwater robots navigating close to such structures. An example is underwater robots operating close to or within a flexible ocean structure such as a fish cage