Abstract
To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.
Highlights
The improved A* obstacle avoidance algorithm improves on the traditional A* algorithm through dynamic heuristic function, search point optimization, and inflection points optimization
A* obstacle avoidance algorithm improves on the traditional A* algorithm through the error was proportional to the measuring distance, with a maximum error of 8.2% within the following three steps: dynamic heuristic function, search point optimization, and inflecset measuring range
The results show that during actual obstacle avoidance flight with different scenarios and flight pacombinations (FPC), the millimeter wave (MMV) radar and monocular camera data fusion and obstacle information extraction method can obtain the accurate distance and contours of obstacle
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The unmanned aerial vehicles (UAV) have been widely used in agriculture, including crop monitoring [1,2], crop yield assessment [3,4], and plant protection [5–7]. Especially through pesticide spraying to control pests and diseases is an important part of agricultural production. Compared to conventional ground-moving plant protection equipment, the plant protection UAV offer clear advantages in terms of terrain adaptation and high efficiency [8]
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