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All Journal GEMA TEKNOLOGI Jurnal IPTEK INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Techne : Jurnal Ilmiah Elektroteknika Journal of Robotics and Control (JRC) Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Jaringan Telekomunikasi JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga)
Rakhmania, Amalia Eka
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Online Digital Image Stabilization for an Unmanned Aerial Vehicle (UAV) Rahmaniar, Wahyu; Rakhmania, Amalia Eka
Journal of Robotics and Control (JRC) Vol 2, No 4 (2021): July (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Unmanned Aerial Vehicle (UAV) video system uses a portable camera mounted on the robot to monitor scene activities. In general, UAVs have very little stabilization equipment, so getting good and stable images of UAVs in real-time is still a challenge. This paper presents a novel framework for digital image stabilization for online applications using a UAV. This idea aims to solve the problem of unwanted vibration and motion when recording video using a UAV. The proposed method is based on dense optical flow to select features representing the displacement of two consecutive frames. K-means clustering is used to find the cluster of the motion vector field that has the largest members. The centroid of the largest cluster was chosen to estimate the rigid transform motion that handles rotation and translation. Then, the trajectory is compensated using the Kalman filter. The experimental results show that the proposed method is suitable for online video stabilization and achieves an average computation time performance of 47.5 frames per second (fps).
Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms Rahmaniar, Wahyu; Rakhmania, Amalia Eka
Journal of Robotics and Control (JRC) Vol 3, No 1 (2022): January
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i1.11024

Abstract

Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment.
Co-Authors Abdul Rasyid Amalia Amalia Ardiansyah, Rizky Aulia, Hillyatul Dwi Fadila Kurniawan Hadiwiyatno Hanafi, Nur Hariyadi, Aad Harvinanda, Akhmad Mukhibuddin Hudiono Hudiono, Hudiono Imammudin , Azam Muzakhim Kusumawardani, Mila Nirmala, Nabila Laila Nurul Hidayati P, Ridho Hendra Yoga Prasetyo, Junaedi Adi Rahmaniar, Wahyu Refiza, Rizky ridho hendra yoga perdana Soelistianto, Farida Arinie Solikh, Sairi Taufik, Mochammad Tobing, Nindi Karynnina Luban Wijayanti, Rieke Adriati Yulianto , Ahmad Wilda