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Tran, Dong LT.
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Electrical & Electronics Engineering

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A novel framework of building operation algorithm for the block of technical diagnostics of aircraft’s automatic control system Vuong, Trung A.; Tran, Dong LT.; Vo, Thanh C.; Nguyen, Minh T.; Tran, Hoang T.
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5799

Abstract

This article presents the problem of designing an automatic control system that is stable against errors and failures of sensors on aircraft. The sensor system has a technical diagnostic block that ensures diagnosis and eliminates typical errors and failures. Based on the determination of the error vector, damage can occur by adding measurement elements corresponding to the measurement parameters to the control system. When there are errors or failures of the sensor elements, the state vector of the system changes and is determined by measurements. The difference between the measured vector components when there are errors, failures and when working normally is the basis of the working algorithm of the failure diagnosis block. The results demonstrate encouraging prospects for practical implementations.
Path planning and obstacle avoidance for UAVs using Theta* and modulated velocity obstacle avoidance with 2D LiDAR Tran, Hoang Thuan; Tran, Dong LT.; Vo, Chi Thanh
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10594

Abstract

This paper proposes a novel framework for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, seamlessly integrating Theta* for global path planning with a simplified modulated velocity obstacle avoidance (MVOA) algorithm for local obstacle avoidance. Theta* generates optimal, smooth paths, while MVOA processes 2D LiDAR data as a single obstacle block to compute modulated velocities, enabling efficient avoidance of static and dynamic obstacles with minimal computational overhead. Compared to MVOA-only navigation, the integration of Theta* and MVOA produced shorter trajectories and faster mission completion with smoother velocity adjustments, demonstrating clear improvements in efficiency and stability. Simulation results show the framework maintains a 0.6 m safety distance and operates at 10 Hz, underscoring its robustness and reliability. The resulting control velocity is transmitted to an ArduPilot-based flight controller via MAVLink, ensuring precise, real-time execution. The current implementation focuses on 2D navigation in a planar environment as a foundation for future 3D expansion, with all results obtained through high-fidelity simulation. Building on these findings, the framework shows strong potential for real-time applications such as swarm UAV coordination, terrain surveying, and indoor navigation, offering a scalable solution for autonomous systems in dynamic settings.
Co-Authors Minh T. Nguyen, Minh T. Tran, Hoang T. Tran, Hoang Thuan Vo, Chi Thanh Vo, Thanh C. Vuong, Trung A.