Bio-Inspired Robotic Fish With Vision Based Target Tracking

The lionfish is an invasive species that out-competes and overcrowds native sh species along the eastern seaboard of the United States and down into the Caribbean. Lionfish populations are growing rapidly. Current methods of monitoring lionfish populations are costly and time intensive. A bio-inspired robotic fish was built to use as an autonomous lionfish tracking platform. Lionfish are tracked visually using an onboard processor. Five different computer vision methods for identification and tracking are proposed and discussed. These include: background subtraction, color tracking, mixture of Gaussian background subtraction, speeded up robust feature (SURF), and CamShift based tracking. Each of these methods were compared and their accuracy analyzed. CamShift based tracking is determined to be the most accurate for this application. Preliminary experiments for system identification and control design are discussed

Crown-of-Thorns Starfish Detection by state-of-the-art YOLOv5

Crown-of-Thorns Starfish outbreaks appeared many decades ago which have threatened the overall health of the coral reefs in Australia’s Great Barrier Reef. This indeed has a direct impact on the reef-associated marine organisms and severely damages the biological diversity and resilience of the habitat structure. Yet, COTS surveillance has been carried out for long but completely by human effort, which is absolutely ineffective and prone to errors. There emerges an urge to apply recent advanced technology to deploy unmanned underwater vehicles for detecting the target object and taking suitable actions accordingly. Existing challenges include but not limited to the scarcity of qualified underwater images as well as superior detection algorithms which is able to satisfy major criteria such as light-weight, high accuracy and speedy detection. There are not many papers in this specific area of research and they can’t fulfill these expectations completely. In this thesis, we propose a deep learning based model to automatically detect the COTS in order to prevent the outbreak and minimize coral mortality in the Reef. As such, we use CSIRO COTS Dataset of underwater images from the Swain Reefs region to train our model. Our goal is to recognize as many starfish as possible while keeping the accuracy high enough to ensure the reliability of the solution. We provide a comprehensive background of the problem, and an intensive literature review in this area of research. In addition, to better align with our task, we use F2 score as the main evaluation metrics in our MS COCO- based evaluation scheme. That is, an average F2 is computed from the results obtained at different IoU thresholds, from 0.3 to 0.8 with a step size of 0.05. In our implementation, we experiment with model architecture selection, online image augmentation, confidence score threshold calibration and hyperparameter tuning to improve the testing performance in the model inference stage. Eventually, we present our novel COTS detector as a promising solution for the stated challenge

Final Report: CoTS Control Program Independent Review

This Report provides an independent assessment of the CoTS Control Program under the coordination of GBRMPA between 2012/13 – 2018/19, reporting against the Terms of Reference..

Thirty years of research on Crown-of-Thorns Starfish (1986–2016): Scientific advances and emerging opportunities

Research on the coral-eating crown-of-thorns starfish (CoTS) has waxed and waned over the last few decades, mostly in response to population outbreaks at specific locations. This review considers advances in our understanding of the biology and ecology of CoTS based on the resurgence of research interest, which culminated in this current special issue on the Biology, Ecology and Management of Crown-of-Thorns Starfish. More specifically, this review considers progress in addressing 41 specific research questions posed in a seminal review by P. Moran 30 years ago, as well as exploring new directions for CoTS research. Despite the plethora of research on CoTS ( > 1200 research articles), there are persistent knowledge gaps that constrain effective management of outbreaks. Although directly addressing some of these questions will be extremely difficult, there have been considerable advances in understanding the biology of CoTS, if not the proximate and ultimate cause(s) of outbreaks. Moving forward, researchers need to embrace new technologies and opportunities to advance our understanding of CoTS biology and behavior, focusing on key questions that will improve effectiveness of management in reducing the frequency and likelihood of outbreaks, if not preventing them altogether

Modelling tools to support the management of crown-of-thorns starfish (Acanthaster cf. solaris) on Australia's Great Barrier Reef

Samuel Matthews studied outbreaks of the crown-of-thorns starfish (COTS) on the Great Barrier Reef. He developed a number of modelling and simulation tools to help predict when and where COTS outbreaks occur. Government agencies are using his results and tools to improve how outbreaks of COTS are managed and controlled on the GBR

Towards a Pantograph-based Interventional AUV for Under-ice Measurements

This paper addresses the design of a novel interventional robotic platform, aiming to perform an autonomous sampling and measurement under the thin ice in the Antarctic environment. We propose a pantograph mechanism, which can effectively generate a constant interaction force to the surface during the contact, which is crucial for reliable measurements. We provide the proof-of-concept design of the pantograph with a robotic prototype with foldable actuation. Preliminary results of the pantograph mechanism and the localisation system are provided, confirming the feasibility of the system

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Prioritising sensory systems for Queensland: An evaluation of alternative sensory systems using multiple criteria analysis

Sensor technology is an extensive field – the Encyclopedia of Sensors comprises 10 volumes of more than 400 chapters. Although sensors have been in use for centuries, sensor technology is rapidly developing now; the digital age provides the opportunity for real-time decision-making based on data received from complex technical systems. New opportunities for sensor technology platforms are becoming available, and the benefits from the application of these platforms have greatly increased

Autonomous Doctrine: Operationalizing the Law of Armed Conflict in the Employment of Lethal Autonomous Weapons Systems

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