Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach)

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dc.contributor.authorAdiuku, Ndidiamaka
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorTang, Gilbert
dc.contributor.authorPlastropoulos, Angelos
dc.date.accessioned2024-04-22T11:56:18Z
dc.date.available2024-04-22T11:56:18Z
dc.date.issued2024-04-02
dc.description.abstractThe integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.en_UK
dc.identifier.citationAdiuku N, Avdelidis NP, Tang G, Plastropoulos A. (2024) Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach). Sensors, Volume 24, Issue 7, April 2024, Article Number 2262en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24072262
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21245
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectautonomous navigationen_UK
dc.subjectobject detectionen_UK
dc.subjectobstacle avoidanceen_UK
dc.subjectmobile roboten_UK
dc.subjectdeep learningen_UK
dc.subjectvisionen_UK
dc.titleImproved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach)en_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-04-01

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