Abstract
Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements.
Highlights
The world’s seas, as a precious asset and an essential element of its ecology, must be protected as an important source of life, wealth and food
This paper proposes an Autonomous underwater vehicles (AUVs) model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an Internet of Things Ocean (IoT) gateway
We propose and evaluate an AUV system designed to collect and interpret underwater images in Mar Menor to track the fan mussel population in real time, using georeferenced mosaics generated from the images by an automatic processing method
Summary
The world’s seas, as a precious asset and an essential element of its ecology, must be protected as an important source of life, wealth and food. We propose and evaluate an AUV system designed to collect and interpret underwater images in Mar Menor to track the fan mussel population in real time, using georeferenced mosaics generated from the images by an automatic processing method. We propose and evaluate an AUV system designed to collect and interpret underwater images to track the fan mussel population in real time, using georeferenced mosaics generated from the images by an automatic processing method. This automated approach is based on DL image processing techniques such as convolutional neural networks (CNN) to detect the position of a possible specimen in a captured photo. The visual servo control and distance estimation systems are outlined in Section 5, the performance is appraised in Section 6, and Section 7 describes a case study in the form of an exploration project
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