The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Path planning and collision avoidance for autonomous surface vehicles I: a review

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe

Supplementary report to the final report of the coral reef expert group: S6. Novel technologies in coral reef monitoring

[Extract] This report summarises a review of current technological advances applicable to coral reef monitoring, with a focus on the Great Barrier Reef Marine Park (the Marine Park). The potential of novel technologies to support coral reef monitoring within the Reef 2050 Integrated Monitoring and Reporting Program (RIMReP) framework was evaluated based on their performance, operational maturity and compatibility with traditional methods. Given the complexity of this evaluation, this exercise was systematically structured to address the capabilities of technologies in terms of spatial scales and ecological indicators, using a ranking system to classify expert recommendations.An accessible copy of this report is not yet available from this repository, please contact [email protected] for more information