Abstract
To solve the issue that the monocular vision vehicle navigation system is limited by the field of vision acquired by the charge-coupled device camera and cannot acquire navigation turning path information throughout the turning process, decreasing the vehicle turning control accuracy, this paper proposed a turning control algorithm based on monocular vision vehicle turning path prediction. Firstly, the camera’s distortion was adjusted. Secondly, the camera imaging model was built, and the turning path’s position information was determined using the imaging position relationship. The vehicle motion model was built in accordance with the vehicle steering mode. Lastly, the cornering trajectory of a vehicle was estimated using the vehicle’s front axle length and front-wheel adjustment data, determining the vehicle turning point and turn operations on the basis of the projected relationship between the vehicle turning track and the turning path position. The experimental results showed that the proposed algorithm can effectively measure the position parameters of the cornering path and complete vehicle cornering control. The maximum absolute error of intercept and slope in turn path position parameters were 0.2525 m and 0.014 m, respectively. The cornering control accuracy was 0.093 m and 0.085 m, which met the vehicle navigation cornering control requirements. At the same time, the research can provide theoretical reference for research on precise navigation control of other cornering vehicles and other path guidance modes.
Highlights
The development of artificial intelligence and digitization has laid the foundation for intelligent animal husbandry
In relation to the above problem, this paper proposes a vehicle based on the monocular vision path prediction control algorithm of turning, turning to the camera calibration at first, using holes in the imaging model of image alignment parameters to solve the calculation of ground actual position parameters that are obtained through building the vehicle motion model to forecast the vehicle movement track of turning, as well as the turning point, implementing corresponding steering actions to realize off-line turning control of vehicles
To verify the feasibility of the vehicle turning control algorithm proposed in this paper, on the basis of the path detection algorithm developed in the early stage, we integrated the steering control algorithm designed in this paper
Summary
The development of artificial intelligence and digitization has laid the foundation for intelligent animal husbandry. To free the farmer from heavy labor, improve the quality of operation, and reduce the risk of zoonosis, intelligent animal husbandry has become an important trend of animal husbandry development and the only way to realize the scale and industrialization of animal husbandry [1,2]. Intelligent machines for patrol inspection and feeding of livestock have been designed for moving and independently completing a series of inspections and feedings during the production process of animal husbandry [3–5]. As an important part of intelligent animal husbandry, autonomous navigation and driving system provides important technical support. Because modern animal husbandry requires many functions, in order to make effective use of resources, the cultivation areas are divided into small areas for management, which is convenient for animal husbandry. The lanes in each cultivation area are very compact [2]. It is difficult to find a suitable turning point for each turn, and the study of turn control algorithm is needed
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