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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Mochurad, Lesia
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An autopilot-based method for unmanned aerial vehicles trajectories control and adjustment Mochurad, Lesia; Alsayaydeh, Jamil; Yusof, Mohd Faizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4154-4166

Abstract

In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.
Parallel rapidly exploring random tree method for unmanned aerial vehicles autopilot development using graphics processing unit processing Mochurad, Lesia; Davidekova, Monika; Mitoulis, Stergios-Aristoteles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp712-723

Abstract

Autonomous air movement systems hold great potential for transforming various industries, making their development essential. Autopilot design involves advanced technologies like artificial intelligence, machine learning, and big data. This paper focuses on developing a parallel rapidly-exploring random tree (RRT) algorithm using compute unified device architecture (CUDA) technology for efficient processing on graphics processing units (GPUs). The study evaluates the algorithm's performance in automated trajectory planning for unmanned aerial vehicles (UAVs). Numerical experiments show that the parallel algorithm outperforms the sequential central processing unit (CPU)-based version, especially as task complexity and state space dimensions increase. In scenarios with numerous obstacles, the parallel algorithm maintains stable performance, making it well-suited for various applications. Comparisons with CPU-based methods highlight the advantages of GPU use, particularly in terms of speed and efficiency. Additionally, the performance of two GPU models, NVIDIA RTX 2070 and T4 is compared, with the T4 demonstrating superior performance for similar tasks. Future research should explore integrating multiple algorithms for a more comprehensive UAV autopilot system. The proposed approach stands out for its stability and practical applicability in real-world autopilot implementations.
Co-Authors Alsayaydeh, Jamil Davidekova, Monika Mitoulis, Stergios-Aristoteles Yusof, Mohd Faizal