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Research on an obstacle detection method for autonomous obstacle avoidance test of unmanned surface vehicle

Published: 28 June 2024 Publication History

Abstract

The performance of an unmanned surface vehicle's (USV) navigation control system directly affects the mission types it can perform. In the USV autonomous obstacle avoidance test, coloured floating balls are usually used as obstacles to test the autonomy and mobility of the USV. As deep learning-based obstacle detection methods requires relatively strong arithmetic skills for the visual detection terminal of the USV, and in order to control the cost of hardware and software, this paper proposes a better applicability method for detecting floating ball from background using RGB channel thresholds in colour space. The RGB distribution characteristics of the floating ball and the sea background under sunny (noon and dusk), cloudy and foggy conditions are studied in comparison, the steps of how to divide the pixel areas of floating ball from the sea and sky background was given, and the noisy pixel removal method were also given. Finally, real video images of trials were used as the data to test the performance of the method which proves its correctness and feasibility.

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  1. Research on an obstacle detection method for autonomous obstacle avoidance test of unmanned surface vehicle

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    ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
    September 2023
    335 pages
    ISBN:9798400708039
    DOI:10.1145/3655532
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 June 2024

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    Author Tags

    1. RGB channel threshold
    2. autonomous obstacle avoidance
    3. obstacle detection
    4. unmanned surface vehicle

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