Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots.

With the development of pesticide substitution technology, ozonated water has been gradually applied in agricultural plant protection. This paper describes our development of an ecological plant protection unmanned aerial vehicle (UAV) that can produce and spray ozonated water while flying. Firstly, this paper carries out the design of the ozonated water system, including the selection of the ozone generator and the gas-liquid mixing method. Secondly, the conceptual design method of the ecological plant protection UAV is introduced, including total weight estimation, propulsion system selection, layout and structure design, battery modeling, center of gravity evaluation, and control system. Then, static analysis was computed in ANSYS Workbench on the UAV fuselage. Finally, the field test verified that the hovering time of the UAV could reach the design requirement of 10 min when it was fully loaded. The effective spraying width (with a height of 2 m and a speed of 3 m/s) is 5.25 m. The UAV was used to spray ozonated water with a concentration of 17 ppm continuously once a day; on day 7, the control effect could reach 76.4% and the reduction rate of the larvae population was 59.3%. Therefore, spraying ozonated water with a concentration of 17 ppm every day by using the ecological plant protection UAV can effectively control broccoli diamondback moth larvae and achieve the control effect of traditional pesticides (Chlorantraniliprole SC).

This paper proposes a local path planning algorithm method named S-TEB (Smooth Time Elastic Band), aimed at fulfilling the requirement of full coverage for ORLMs (Orchard Robotic Lawn Mowers) during mowing operations. Firstly, by analyzing the tracking control mode of ORLMs in operational scenarios, control points are selected reasonably. Subsequently, utilizing B-spline curves, the path is optimized to generate the optimal trajectory and speed for ORLMs that satisfy multiple objectives and constraints. Finally, multiple simulations and field experiments were conducted in actual operational environments, with a speed of 0.6 m/s. Experimental results show that in scenarios involving obstacle avoidance, the minimum distance between the automatic lawnmower and the outer contour of obstacles is 4 cm. Compared to the traditional TEB planning algorithm, there is a 4.23% increase in mowing coverage area. These findings provide theoretical and technical support for local path planning in the operational scenarios of ORLMs.

With the advancement of modern agricultural technology, precision seeding has emerged as a critical approach to enhancing the crop yield and quality. Consequently, a garlic seeder insertion mechanism was developed to improve the accuracy and efficiency of garlic seeding. The single-seed extraction mechanism and the adjustment mechanism for the garlic clove direction were designed based on the appearance dimensions of garlic cloves, enabling precise single-seed selection and orientation. A kinematic model of the insertion planting process was established, with key parameters meticulously described and analyzed, providing theoretical support for determining optimal insertion parameters. A timing sequential control method was adopted to accurately control the periodic motion of the insertion planting mechanism. A speed detection device was utilized to monitor the travel speed of the crawler-type chassis and a rotational speed controller was developed to accurately regulate the rotational speed of the insertion mechanism, ensuring uniform planting distances. Field trials demonstrated that when the preset planting distance was set at 150 mm and sowing operations were conducted at speeds of 0.10 m/s, 0.15 m/s, and 0.20 m/s, the average sowing spacing values were 148 mm, 149 mm, and 151 mm, respectively, the maximum sowing spacing error and root mean square (RMS) error were 30 mm and 7 mm, with an average error of less than 10 mm, and the maximum coefficient of variation was 0.046. The upright rate exceeded 85%, and the missing seeding rate was below 5%. The above results indicated that the designed garlic planting machine insertion mechanism and control method conform to the agronomic requirements for garlic sowing operations.

Determination of application parameters for cotton defoliants in the Yellow River Basin

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.