Mobile robot obstacle detection and avoidance with NAV-YOLO

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dc.contributor.authorAdiuku, Ndidiamaka
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorTang, Gilbert
dc.contributor.authorPlastropoulos, Angelos
dc.contributor.authorDiallo, Yanis
dc.date.accessioned2024-04-09T13:12:58Z
dc.date.available2024-04-09T13:12:58Z
dc.date.issued2024-03-22
dc.description.abstractIntelligent robotics is gaining significance in Maintenance, Repair, and Overhaul (MRO) hangar operations, where mobile robots navigate complex and dynamic environments for Aircraft visual inspection. Aircraft hangars are usually busy and changing, with objects of varying shapes and sizes presenting harsh obstacles and conditions that can lead to potential collisions and safety hazards. This makes Obstacle detection and avoidance critical for safe and efficient robot navigation tasks. Conventional methods have been applied with computational issues, while learning-based approaches are limited in detection accuracy. This paper proposes a vision-based navigation model that integrates a pre-trained Yolov5 object detection model into a Robot Operating System (ROS) navigation stack to optimise obstacle detection and avoidance in a complex environment. The experiment is validated and evaluated in ROS-Gazebo simulation and turtlebot3 waffle-pi robot platform. The results showed that the robot can increasingly detect and avoid obstacles without colliding while navigating through different checkpoints to the target location.en_UK
dc.identifier.citationAdiuku N, Avdelidis NP, Tang G, et al., (2024) Mobile robot obstacle detection and avoidance with NAV-YOLO. International Journal of Mechanical Engineering and Robotics Research, Volume 13, Issue 2, March 2024, pp. 219-226en_UK
dc.identifier.issn2278-0149
dc.identifier.urihttps://doi.org/10.18178/ijmerr.13.2.219-226
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21160
dc.language.isoen_UKen_UK
dc.publisherEJournal Publishingen_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectautonomous navigationen_UK
dc.subjectobject detectionen_UK
dc.subjectobstacle avoidanceen_UK
dc.subjectmobile roboten_UK
dc.subjectdeep learningen_UK
dc.titleMobile robot obstacle detection and avoidance with NAV-YOLOen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-10-10

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