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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Program Kreativitas Mahasiswa - Karsa Cipta Journal Of English Language and Education Prosiding Seminar Nasional Teknoka JEPA (Jurnal Ekonomi Pertanian dan Agribisnis) Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Buletin Ilmiah Sarjana Teknik Elektro Health Information : Jurnal Penelitian Jurnal Karinov Best Journal (Biology Education, Sains and Technology) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) Frontier Energy System and Power Engineering Belantika Pendidikan Jurnal Graha Pengabdian International Journal of Robotics and Control Systems Proceedings of the International Conference on Nursing and Health Sciences Ilmu Komputer untuk Masyarakat International Journal of Health Science & Medical Research SinarFe7 IJIGAEd: Indonesian Journal of Islamic Golden Age Education Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Publikasi Ilmu Komputer dan Multimedia JURNAL MUARA PENDIDIKAN JKMP (Jurnal Kebijakan dan Manajemen Publik) Al Basirah Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal INFOTEL Jurnal Ventilator: Jurnal Riset Ilmu Kesehatan dan Keperawatan Prosiding Seminar Nasional Pengabdian kepada Masyarakat (SINAPMAS) Emitor: Jurnal Teknik Elektro
Dyah Lestari
Universitas Negeri Malang
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Journal : International Journal of Robotics and Control Systems

 1 
Real-Time Obstacle Detection for Unmanned Surface Vehicle Maneuver Anik Nur Handayani; Ferina Ayu Pusparani; Dyah Lestari; I Made Wirawan; Aji Prasetya Wibawa; Osamu Fukuda
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1147

Abstract

The rapid advancement and increasing demand for Unmanned Surface Vehicle (USV) technology have drawn considerable attention in various sectors, including commercial, research, and military, particularly in marine and shallow water applications. USVs have the potential to revolutionize monitoring systems in remote areas while reducing labor costs. One critical requirement for USVs is their ability to autonomously integrate Guidance, Navigation, and Control (GNC) technology, enabling self-reliant operation without constant human oversight. However, current study for USV shown the use of traditional method using color detection which is inadequate to detect object with unstable lighting condition. This study addresses the challenge of enabling Autonomous Surface Vehicles (ASVs) to operate with minimal human intervention by enhancing their object detection and classification capabilities. In dynamic environments, such as water surfaces, accurate and rapid object recognition is essential. To achieve this, we focus on the implementation of deep learning algorithms, including the YOLO algorithm, to empower USVs with informed navigation decision-making capabilities. Our research contributes to the field of robotics by designing an affordable USV prototype capable of independent operation characterized by precise object detection and classification. By bridging the gap between advanced visualization techniques and autonomous USV technology, we envision practical applications in remote monitoring and marine operations with object detection. This paper presents the initial phase of our research, emphasizing significance of deep learning algorithms for enhancing USV navigation and decision-making in dynamic environmental conditions, resulting in mAP of 99.51%, IoU of 87.80%, error value of the YOLOv4-tiny image processing algorithm is 0.1542.
Improving Efficiency and Effectiveness of Wheeled Mobile Robot Pathfinding in Grid Space Using a Genetic Algorithm with Dynamic Crossover and Mutation Rates Lestari, Dyah; Sendari, Siti; Zaeni, Ilham Ari Elbaith; Arifin, Samsul; Sari, Rina Dewi Indah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1573

Abstract

Incorrect parameter tuning of crossover and mutation rates in Genetic Algorithms (GA) can negatively impact their effectiveness and efficiency in mobile robot pathfinding. This study focuses on improving the performance of wheeled mobile robots in grid-based environments by introducing a Dynamic Crossover and Mutation Rates (DCMR) strategy within the GA framework. The primary contribution of this research is enhancing the efficiency and effectiveness of mobile robot pathfinding, resulting in shorter average path lengths and faster convergence times. Additionally, this method addresses the challenge of selecting appropriate GA parameters while increasing the algorithm's adaptability to different phases of the search process. The DCMR approach involves linearly increasing the crossover rate by 10% (from 0% to 100%) and decreasing the mutation rate by 10% (from 100% to 0%) over every 10 generations during the GA's evolution. Unlike fixed parameter tuning or exponential and sigmoid parameter tuning—both of which require trial and error to determine optimal values—the DCMR method provides a systematic and efficient solution without additional computational cost. Experiments were conducted across eight scenarios featuring varying distances between the start and target points, with two obstacles randomly placed in the robot's environment. The results showed that implementing the DCMR method consistently identified the optimal path, reduced average path lengths by 0.99%, and accelerated algorithm convergence by 48.39% compared to fixed parameter tuning. These findings demonstrate that the DCMR method significantly enhances the performance of GAs for mobile robot pathfinding, offering a reliable and efficient approach for navigating complex environments.
Co-Authors Achmad Safi’i Aditama Yudha Atmanegara Agus Suprayitno Aji Prasetya Wibawa Al Afrin Uwais, Tito Alqodri, Febrianto Anik N. Handayani Anik Nur Handayani Arfiyansyah, Rizky Arif Widodo, Baskoro Aripriharta - Arya Mahesa Jenar Atik Umamah Aya Sofia Mufti Ayu Kusuma Dewi Azhar Ahmad Smaragdina Benny Agung Prasetyo Cahyaning Wulandari Cahyaning Citta Anindya, Citta Desyandri Desyandri Dewi Anggraini Dito Valentino Dityo Kreshna Argeshwara Dwika Mutiara Abriani Ekananda, Thoriq Bachtiar Yusuf Falah, Moh. Zainul Febrianto Alqodri Ferina Ayu Pusparani Fido Arya Kusuma Firmansyah, M Ferrari Giri Wahyu Wiriasto Hakkun Elmunsyah Harits Ar Rosyid Haya Mei Fatma Bela Hikma, Waode Erty Hikmah, Wa Ode Erty I Made Wirawan Imam Agus Basuki Ira Kumalasari Irham Fadlika Joumil Aidil Saifuddin Khoirunnisa, Umi Kumala Puluhulawa Kumalasari, Ira M Rafli Dwi Suryanto M Rodhi Faiz M. Alfian Mizar M. Farrel Akbar Firzatullah M. Husni Tamrin Maghfiroh, Ila Mahadir Muhamad Erfin Abdilah Mochtar, Norrima Mohammad Arief Mokh Sholihul Hadi Muhamad Syamsu Iqbal Muhammad Afnan Habibi Muhammad Khusairi Osman Mukin, Rovina Nini Mustikawati Mustikawati Nafisah, Wardatun Nathaniela Azaria Ashari Ningsih, Suwita Novi Rosanti Nuraina Sari Nusantar, Alrizal Akbar Nusantar Akbar Osamu Fukuda Pramudita, Devi Fitria Purbo Suwasono Ramadhiansyah, Fatqan Ratnasari, Nur Qomariyah Dyah Rina Dewi Indahsari Riski Achmad Fauzi Rizal, Mabrul Samsul Arifin Setiadi Cahyono Putro Setumin, Samsul Siti Sendari Soraya Norma Mustika Sugeng Firmansyah, Sugeng Sujito Sujito Suryani, Ani Wilujeng Swasono Rahardjo Syaad Patmanthara Syah, Abdullah Iskandar Syaiful Hamzah Nasution Syifaul Fuada Tiya Nurul Khusna Wibowo, Sulton Ari Yandhika Surya Akbar Gumilang Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zainul Falah, Moh Zalhani, Muhammad Rashif