Stein M. Nornes

Autonomous robotic intervention using ROV: An experimental approach

Using an experimental approach, this paper proposes a semi-autonomous agent architecture for a remotely operated vehicle (ROV). The system is inspired by Behavior- and Reactive-based architectures using stimulus response blocks to segment behavior. The capability and limitations of the system is demonstrated through a field experiment, where the goal is to approach and localize a structure of interest (SOI). The system is tested using Hardware-In the-Loop (HIL) simulations before deployment. The motivation for our approach is testing and verification of architecture feasibility in an environment similar to an operational situation. The results from the field campaigns demonstrate the ROV agent able to execute an inspection type mission, navigating to the SOI from surface, while avoiding obstacles.

Use of an Autonomous Surface Vehicle reveals small-scale diel vertical migrations of zooplankton and susceptibility to light pollution under low solar irradiance

Using new enabling technologies, we document behavioral patterns and susceptibility to light pollution never previously seen. Light is a major cue for nearly all life on Earth. However, most of our knowledge concerning the importance of light is based on organisms’ response to light during daytime, including the dusk and dawn phase. When it is dark, light is most often considered as pollution, with increasing appreciation of its negative ecological effects. Using an Autonomous Surface Vehicle fitted with a hyperspectral irradiance sensor and an acoustic profiler, we detected and quantified the behavior of zooplankton in an unpolluted light environment in the high Arctic polar night and compared the results with that from a light-polluted environment close to our research vessels. First, in environments free of light pollution, the zooplankton community is intimately connected to the ambient light regime and performs synchronized diel vertical migrations in the upper 30 m despite the sun never rising above the horizon. Second, the vast majority of the pelagic community exhibits a strong light-escape response in the presence of artificial light, observed down to 100 m. We conclude that artificial light from traditional sampling platforms affects the zooplankton community to a degree where it is impossible to examine its abundance and natural rhythms within the upper 100 m. This study underscores the need to adjust sampling platforms, particularly in dim-light conditions, to capture relevant physical and biological data for ecological studies. It also highlights a previously unchartered susceptibility to light pollution in a region destined to see significant changes in light climate due to a reduced ice cover and an increased anthropogenic activity.

Automatic relative motion control and photogrammetry mapping on steep underwater walls using ROV

This paper describes an equipment setup and motion control strategy for automated visual mapping of steep underwater walls using a Remotely Operated Vehicle (ROV) equipped with a horizontally facing DVL to provide vehicle velocity and distance measurements relative to the underwater wall. The still images recorded by the stereo cameras of the ROV are post-processed into a 3D photogrammetry model using a combination of commercially available software and freeware. The system was implemented on an ROV and tested on a survey of a rock wall in the Trondheimsfjord in April 2016. The main scientific contribution is in the development of the motion control strategy for distance keeping using measurements from a DVL mounted in an arbitrary orientation.

Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna

Identification of benthic megafauna is commonly based on analysis of physical samples or imagery acquired by cameras mounted on underwater platforms. Physical collection of samples is difficult, particularly from the deep sea, and identification of taxonomic morphotypes from imagery depends on resolution and investigator experience. Here, we show how an Underwater Hyperspectral Imager (UHI) can be used as an alternative in situ taxonomic tool for benthic megafauna. A UHI provides a much higher spectral resolution than standard RGB imagery, allowing marine organisms to be identified based on specific optical fingerprints. A set of reference spectra from identified organisms is established and supervised classification performed to identify benthic megafauna semi-autonomously. The UHI data provide an increased detection rate for small megafauna difficult to resolve in standard RGB imagery. In addition, seafloor anomalies with distinct spectral signatures are also detectable. In the region investigated, sediment anomalies (spectral reflectance minimum at ~675 nm) unclear in RGB imagery were indicative of chlorophyll a on the seafloor. Underwater hyperspectral imaging therefore has a great potential in seafloor habitat mapping and monitoring, with areas of application ranging from shallow coastal areas to the deep sea.

Vision based obstacle avoidance and motion tracking for autonomous behaviors in underwater vehicles

Performing reliable underwater localization and maneuvering of Remotely Operated underwater Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) near nature protection areas, historical sites or other man-made structures is a difficult task. Traditionally, different sensing techniques are exploited with sonar being the most often used to extract depth information and to avoid obstacles. However, little has been published on complete control systems that utilize robotic vision for such underwater applications. This paper provides a proof of concept regarding a series of experiments investigating the use of stereo vision for underwater obstacle avoidance and position estimation. The test platform has been a ROV equipped with two industrial cameras and external light sources. Methods for underwater calibration, disparity map and 3D point cloud processing have been used, to obtain more reliable information about obstacles in front of the ROV. Results from laboratory research work and from field experiments demonstrate that underwater obstacle avoidance with stereo cameras is possible and can increase the autonomous capabilities of ROVs by providing appropriate information for navigation, path planning, safer missions and environment awareness.

Motion Control of ROVs for Mapping of Steep Underwater Walls

This chapter describes an equipment setup and motion control strategy for automated visual mapping of steep underwater walls using a remotely operated vehicle (ROV) equipped with a horizontally facing doppler velocity logger (DVL) to provide vehicle velocity and distance measurements relative to the underwater wall. The main scientific contribution is the development of the motion control strategy for distance keeping and adaptive orientation using measurements from a DVL mounted in an arbitrary orientation. Autonomy aspects concerning this type of mapping operation are also discussed. The still images recorded by the stereo cameras of the ROV are post-processed into a 3D photogrammetry model using a combination of commercially available software and freeware. The system was implemented on an ROV and tested on a survey of a rock wall in the Trondheimsfjord in April 2016.