TY - JOUR T1 - Derin Öğrenme Yöntemleriyle Kapalı Alan Temizlik Robotunun Hareket Planlamasının Optimizasyonu TT - Optimization of Motion Planning of Indoor Cleaning Robot with Deep Learning Methods AU - Sakarya, Deniz AU - Dandıl, Beşir Y2 - 2025 DO - 10.35234/fumbd.1738019 JF - Fırat Üniversitesi Mühendislik Bilimleri Dergisi PB - Fırat Üniversitesi WT - DergiPark SN - 1308-9072 SP - 1 EP - 11 VL - 38 IS - 1 LA - tr AB - Günümüzde teknolojinin gelişmesiyle birlikte mobil robotlar da hayatımıza giriş yapmıştır. Mobil robotların kullanım alanlarından biri olan temizlik robotları, insan yaşamını kolaylaştıran araçlardan biri haline gelmiştir. Bunun en temel özelliği de bulunduğu ortamı insanların yerine temizlemesidir. Bu çalışmada, otonom bir şekilde bulunduğu ortamın zemin temizleme işlemlerini gerçekleştirebilen robot süpürge gerçekleştirilmiştir. Geliştirilen robot süpürge, diğer robot süpürgelerden farklı olarak karşısına bir nesne çıkması durumunda yol planlamasını düzenleme özelliğine sahiptir. Yol planlamasını düzenleyebilmesi için derin öğrenme yöntemi kullanılmıştır. Derin öğrenme sayesinde, önüne eğitim aşamasında öğretilen cisimlerden herhangi birisinin çıkması durumunda oluşturulan algoritmaya göre yol planlamasını düzenleyebilmektedir. Bu sayede robot süpürge cisimlere takılmadan gezinme işlemine devam edebilmektedir. KW - Derin öğrenme KW - yol planlaması KW - haritalama KW - robotik N2 - Today, with the advancement of technology, mobile robots have entered our lives. Cleaning robots, one of the applications of mobile robots, have become tools that make human life easier. Their most fundamental feature is that they clean their environment on behalf of humans. In this study, a robot vacuum cleaner capable of autonomously cleaning the floors of its environment was developed. Unlike other robot vacuum cleaners, the developed robot vacuum cleaner has the ability to adjust its path plan when it encounters an object. Deep learning was used to adjust this path plan. Thanks to deep learning, it can adjust its path plan according to the algorithm created during the training phase if any of the objects encountered are encountered. This allows the robot vacuum cleaner to continue navigating without getting stuck. CR - Boztaş G, Aydoğmuş Ö. Implementation of pure pursuit algorithm for nonholonomic mobile robot using robot operating system. BAJECE, 2021; 9(4), 337-341. CR - https://www.mathworks.com/discovery/path-planning.html (Erişim tarihi: 07.07.2025) CR - Sohan M, Sai Ram T, Rami Reddy CV. A review on yolov8 and its advancements. In International Conference on Data Intelligence and Cognitive Informatics, 2024; Singapore: Springer. pp. 529-545. CR - Eren A, Doğan H. Design and implementation of a cost effective vacuum cleaner robot. TUJE, 2022; 6(2), 166-177. 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