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Development of a Vision-Based Situational Awareness Capability for Unmanned Surface Vessels

Computer vision-based technique gives USVs enhanced autonomous navigation capabilities.

Using unmanned surface vessels (USVs) for “dull, dirty and dangerous missions” is gaining traction in recent years as it removes the human element from a potentially life-threatening environment in missions such as mine hunting or maritime interdiction. Current USVs rely on human operators sitting in remote control stations to monitor the vessels’ surroundings and perform collision detection and avoidance. This reliance on the human operator constrains the operating envelope of the USV as it requires a high bandwidth and low latency communication link for safe operations, especially in waters with heavy traffic.

An autonomous navigation capability needs to be incorporated into future USVs to fully exploit the advantages of operating them. To achieve this desired outcome, the USV must have situational awareness of its surroundings. This research adopts a systems engineering approach for identifying the capability gap in today's USV and the factors that drive the need for a USV with autonomous navigation capability. A functional decomposition is completed to identify the functions required for the USV to perform autonomous navigation. A computer vision-based technique is used to implement one of the functions identified through the functional decomposition.

A prototype of the Republic of Singapore Navy's MCM USV undergoing sea trials in Singapore.

The algorithm, developed in MATLAB, converts the video into individual frames before enhancing them for further processing. The images undergo processing using edge detection and morphological structuring techniques before information is derived from the processed images. The algorithm was tested with images from color video sources as well as infrared (IR) video sources.

This work was done by Ying Jie Benjemin Toh for the Naval Postgraduate School. NPS-0004

Topics:
Aerospace Artificial intelligence (AI) Automation Autonomous vehicles Defense Imaging Imaging and visualization Machine Vision Machine learning Marine vehicles and equipment Mathematical models Optics Product development Research and development Robotics Simulation Software Simulation and modeling Systems engineering Transportation Unmanned Underwater and Surface Vehicles (UUW/USV) Unmanned ground vehicles Vehicles Video
This Brief includes a Technical Support Package (TSP).
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Development of a Vision-Based Situational Awareness Capability for Unmanned Surface Vessels

(reference NPS-0004) is currently available for download from the TSP library.

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    Aerospace & Defense Technology Magazine

    This article first appeared in the May, 2018 issue of Aerospace & Defense Technology Magazine (Vol. 3 No. 3).

    Read more articles from this issue here.

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    Overview

    I apologize, but I cannot provide a summary of the document as I do not have access to its content. However, based on the information you provided, I can infer that the thesis by Ying Jie Benjemin Toh focuses on enhancing the situational awareness of unmanned surface vessels (USVs) through vision-based systems. It addresses the limitations of current USVs that rely on human operators for collision avoidance and emphasizes the importance of developing autonomous capabilities to improve operational efficiency and safety.

    The research likely employs a systems engineering approach to identify existing capability gaps in USVs and proposes innovative algorithms to enhance navigation and safety. The goal is to create a more autonomous system that can operate effectively in various maritime environments, reducing the need for human intervention and increasing the reliability of USVs in complex situations.

    If you have specific questions about the document or need information on a particular aspect, feel free to ask!

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