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All Journal JURNAL ELEKTRO Seminar Nasional Informatika (SEMNASIF) Jurnal Ilmiah KOMPUTASI Jurnal Informatika Universitas Pamulang J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Muara Ilmu Sosial, Humaniora, dan Seni Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JIIP (Jurnal Ilmiah Ilmu Pendidikan) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan dan Teknologi Indonesia International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science
Julius Bata
Universitas Katolik Indonesia Atma Jaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : International Journal of Robotics and Control Systems

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Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
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.v5i1.1757

Abstract

Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning.
Enhanced Advanced Multi-Objective Path Planning (EAMOPP) for UAV Navigation in Complex Dynamic 3D Environments Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry; Saputro, Pujo Hari; Makin, See Jong; Alamsyah, Alamsyah
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1759

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as vital tools in diverse applications, including disaster response, surveillance, and logistics. However, navigating complex, obstacle-rich 3D environments with dynamic elements remains a significant challenge. This study presents an Enhanced Advanced Multi-Objective Path Planning (EAMOPP) model designed to address these challenges by improving feasibility, collision avoidance, and path smoothness while maintaining computational efficiency. The proposed enhancement introduces a hybrid sampling strategy that combines random sampling with gradient-based adjustments and a refined cost function that prioritizes obstacle avoidance and path smoothness while balancing path length and energy efficiency. The EAMOPP was evaluated in a series of experiments involving dynamic environments with high obstacle density and compared against baseline algorithms, including A*, RRT*, Artificial Potential Field (APF), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Results demonstrate that the EAMOPP achieves a feasibility score of 0.9800, eliminates collision violations, and generates highly smooth paths with an average smoothness score of 9.3456. These improvements come with an efficient average execution time of 6.6410 seconds, outperforming both traditional and heuristic-based methods. Visual analyses further illustrate the model's ability to navigate effectively through dynamic obstacle configurations, ensuring reliable UAV operation. Future research will explore optimizations to further enhance the model's applicability in real-world UAV missions.
Co-Authors Airlangga, Gregorius Alamsyah - Anggipranoto, Eusebius Vercelli Brian Antonio, Marcel Charmelita, Pauelina Clara Ika Sari Budhayanti Denny Jean Cross Sihombing Denny Jean Cross Sihombing Dominikus Jarvis Dominikus Jarvis Dominikus Vieri Tegar Linestyo Henoch Juli Christanto Jarvis, Dominikus Laura Imanuelle Defira Makin, See Jong Maria Angela Kartawidjaja Melisa Mulyadi Nugroho, Oskar Ika Adi Pijar Karisma Prihanto Pranowo Pranowo Prasetyaningrum, Putri Taqwa Pujo Hari Saputro Salvator Sewi Sangi Stephen Aprius Sutresno, Stephen Aprius Sugianto, Lai Ferry Sutanto, Daniel Denis Suyoto Suyoto Theresia Ghozali W Pandjaitan, MM Lanny Wijayanti, Linda