Obstacle Avoidance and Trajectory Tracking Control of a Quadrotor UAV Under Wind Disturbance

Cai, Zhihao; Hao, Zeyu; Zhao, Jiang; Wang, Yingxun

doi:10.1007/978-981-19-6613-2_585

Cited by 2 publications

(1 citation statement)

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“…After the image acquired by the photosensitive element (OV7725) on the OpenMV module is filtered, the color block of the image is detected and binarized to find the guideline. [4,5] After that, the guideline is regressed to obtain the straight-line parameters of the guideline. The more important ones are the angle (theta) and the position distance (rho).…”

Section: Openmv Tracing Principlementioning

confidence: 99%

Dual-vehicle co-working system based on OpenMV

Du,

Cai

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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The dual-vehicle system uses TI's 32-bit high performance, low-cost, low-power MSP432P401R as a control kernel to design a car-following system for a given multitasking situation. The vehicles have a controllable speed in this system, follow the track, and are capable of performing multiple tasks in conjunction with each other. The main body of the vehicle includes an encoder to obtain the current wheel speed, a greyscale detector, an OpenMV to obtain the track information, and an ultrasound distance measurement module to control the vehicle distance. In addition, the dual vehicles communicate with each other through Bluetooth modules to ensure that the vehicle tracking system can start and stop nearly simultaneously. During the driving process, both cars can smoothly follow the black track and perform the corresponding tasks of overtaking and parking. This system meets the requirements of design and the operation process shows excellent performance.

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Section: Openmv Tracing Principlementioning

confidence: 99%