Design of four rotor aircraft with obstacle avoidance

Haochen Wang; Hao Mei; Yaozhong Hu

doi:10.31686/ijier.vol10.iss5.3570

Authors

Haochen Wang

Shanghai University of Engineering Sciences
Hao Mei

Shanghai University of Engineering Sciences
Yaozhong Hu

Shanghai University of Engineering Science

DOI:

https://doi.org/10.31686/ijier.vol10.iss5.3570

Keywords:

quadrotor, gyroscope, optical flow sensor, PID

Abstract

The system uses TM4C123G as the core of quadrotor autonomous vehicle control, which consists of flight control module, power supply module, motor speed control module, optical flow sensing module, and target tracking identification module. The flight control module includes angle sensor, gyroscope, and TLS1401-LF module. The flight control processes the collected data through the chip (TM4C123G), and processes the data with PID control algorithm, while solving the PWM increment and decrement needed for the corresponding motor, adjusting the motor in time and adjusting the flight attitude. The binocular camera identifies the color of the pole tower and measures the distance, so that the distance between the aircraft and the nearest point of the pole tower is kept within 50±10cm. After detecting the red (green) tower as the center, fly around the tower clockwise (counter) for one week (top view). Finally, the OV7725 camera is used to identify the solid black circle mark of the landing point and land smoothly and accurately in the target area, thus realizing an efficient robot around the barrier.

Author Biographies

Haochen Wang, Shanghai University of Engineering Sciences

School of Electronic and Electrical Engineering.
Hao Mei, Shanghai University of Engineering Sciences

School of Electronic and Electrical Engineering.
Yaozhong Hu, Shanghai University of Engineering Science

School of Electronic and Electrical Engineering.

References

C. Zhao, Y. Liu, L. Chen, F. Li, and Y. Man. "Status and Prospects of Multi-UAV Path Planning with Meta-Heuristic Oriented Algorithms." Control and Decision. 2021: doi: 10. 13195/j.kzyjc.2021.1210.

Y. Zhang." Automatic UAV flight path planning method based on Bayesian decision making." Computer Measurement and Control, 2021.

W. Wang, H. Z. Huang, and R. Z. Li. "Indoor autonomous obstacle avoidance for UAVs based on improved artificial potential field method." Electronic Design Engineering, 2021, 29(18):95-98.

N. Wang, J. Dai, J. Ying, Y. Li, L. Lu. "Simulation of multiple UAV trajectory planning based on adaptive extended potential field". Journal of System Simulation, 2021, 33(09):2147-2156.

Y. Chen, X. Zhai, Y. Song, M. Wu, X. Zhang, and S. Cao. "UAV high-precision localization and vision auto-tracking fusion technology." Combined Machine Tools and Automated Machining Technology.2021, (09):103-106.

K. Li, Y. Lu, S. Bao, and P. Xu. "UAV 3D obstacle avoidance planning based on improved RRT algorithm." Computer Simulation, 2021, 38(08):59-63+96.

Y. Cheng and T. Zheng. "Deep learning for UAV binocular vision obstacle avoidance research." Electro-Optics and Control, 2021.

F. Song, and X. Lu. "UAV obstacle avoidance strategy based on coordinate transformation technology." Electrical Technology, 2021, 22(07):53-59.

Y. Cao. "Research and implementation of key technologies for unmanned aircraft obstacle avoidance flight system." University of Electronic Science and Technology, MA thesis, 2021.