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Subject: search.filters.subject.Autonomous navigation

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    "Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation
    (Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, N
    The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.
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    PublicationOpen Access
    Advancing Object Detection: A Narrative Review of Evolving Techniques and Their Navigation Applications
    (Institute of Electrical and Electronics Engineers Inc., 2025-03-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray Am, I
    Object detection plays a pivotal role in advancing computer vision systems by enabling machines to perceive and interact intelligently with their environments. Despite significant advancements, comprehensive exploration of its evolution and applications in navigation remains underrepresented. This review paper examines the evolution of object detection technologies, from early methodologies to contemporary advancements, and their critical role in navigation tasks. The emphasis was on the significance of contextual learning in enhancing object detection performance by leveraging spatial and temporal information. Furthermore, the limitations of conventional approaches that rely heavily on hand-engineered features are examined. It is then demonstrated that contextual learning facilitates automated feature extraction, resulting in improved accuracy exceeding a 50% increase and adaptability in diverse applications. The review concludes by outlining future trends and opportunities for further advancements in object detection and, underscoring its transformative impact on autonomous navigation and beyond. In summary, this review contributes to a comprehensive understanding of object detection technologies by offering insights into their evolution, highlighting their applications in navigation, and providing guidance for future research in context-aware systems.