To reduce the cost of machinery and manual operation, greatly improve the efficiency of maize sowing, and solve the problems of slow sowing speed, unstable operation quality, and the difficult monitoring of the sowing process of traditional seeders, a control system for an electrically driven precision maize seeder based on the CANopen protocol was designed. In this system, an STM32 is used as the main controller, and the vehicle terminal is used to set the operating parameters, such as the spacing of sowing plants and the number of holes in the metering plate. The GPS receiver is used to collect the forward speed of the tractor. An infrared photoelectric sensor is used to monitor the working state of the seeder. In this study, tests were conducted on different evaluation indices. The results showed that the detection accuracy of the photoelectric sensor reached 99.8% and the fault alarm rate reached 100%. The qualified rate of sowing was more than 91.0%. Based on indoor test results, the qualified rate was higher when the grain spacing was larger. The field test showed, in terms of the seeding performance, that the control system had good stability. When the grain spacing was set to 20 cm and the operating speed was 6~12 km/h, the qualified index was more than 89% and the reseeding index was less than 1.93%. The variation in sowing performance between different monomers was small, and the seeding performance was good. The control system helps to improve the performance of the seeder.

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

With the advantages of high measurement accuracy and wide detection range, LiDARs have been widely used in information perception research to develop agricultural robots. However, the internal configuration of the laser transmitter layout changes with increasing sensor working duration, which makes it difficult to obtain accurate measurement with calibration files based on factory settings. To solve this problem, we investigate the intrinsic calibration of multi-beam laser sensors. Specifically, we calibrate the five intrinsic parameters of LiDAR with a nonlinear optimization strategy based on static planar models, which include measured distance, rotation angle, pitch angle, horizontal distance, and vertical distance. Firstly, we establish a mathematical model based on the physical structure of LiDAR. Secondly, we calibrate the internal parameters according to the mathematical model and evaluate the measurement accuracy after calibration. Here, we illustrate the parameter calibration with three steps: planar model estimation, objective function construction, and nonlinear optimization. We also introduce the ranging accuracy evaluation metrics, including the standard deviation of the distance from the laser scanning points to the planar models and the 3σ criterion. Finally, the experimental results show that the ranging error of calibrated sensors can be maintained within 3 cm, which verifies the effectiveness of the laser intrinsic calibration.

The uniformity of the wheat distribution within and between rows has a significant effect on crop population structure, leading to decreased yield as nonuniformity increases. Traditional drills are influenced by soil porosity and flatness in the field, making accurate control of sowing depth and amount challenging and resulting in an uneven spatial distribution of gramineous seedlings. Precision cave-sowing technology effectively enhances wheat population distribution uniformity. However, owing to limitations in existing mechanical precision cave planters, their operational speed is lower than that of drill planters. To address these issues, this study designed an air-suction precision wheat seed dispenser, described its basic structure and working principle, and developed a seed mechanical model. A theoretical analysis was conducted on the working process and key components of the seed feeder. A suitable mould hole diameter was determined to be 1.6~2.0 mm, and the rotation speed range for the seed plate was found to be 65~85 r·min⁻¹. Fluent simulations were used to determine the influence of orifice type on gas chamber flow fields; DEM-CFD-coupled simulation identified an appropriate negative pressure range of 2.6~3.4 kPa for optimal performance during seeding operations. Orthogonal experiments were carried out with mould hole diameter, negative pressure size, and seed plate speed as test factors alongside a qualification index, multiple sowing index, and missed sowing index as response indicators—leading to regression equation establishment, which yielded the optimal parameter combination: mould hole diameter at 1.8 mm; gas chamber negative pressure at 3.2 kPa; and a seed plate speed of 74 r·min⁻¹, with the corresponding forwards speed of the machine being 7 km·h⁻¹—resulting in a qualification index of 91.66%, multiple sowing index of 5.98%, and missed sowing index of 2.36%. This pneumatic suction type wheat precision seeder achieves equivalent operational speeds as traditional drills while enabling precision seeding.

In view of the challenges faced by cotton dibbler in Xinjiang under high-speed operation, a novel crawler-type delayed hole-forming device has been designed to address the seed throwing and floating issues in high-speed cotton dibbling in Xinjiang, enhancing the duck bill’s performance. This mechanism increases the sowing speed to 6 km/h by extending the duck bill horizontally. Utilizing agronomic principles, the mechanism’s layout and key components were optimized for efficient hole-forming. DEM and multi-body dynamics simulations were employed to analyze the motion, focusing on the fixed the tilt angle of the duck bill (A), the depth of the duck bill hole-forming into the soil (B), and the angle of rotation of the moving duck bill (C) as factors affecting hole dimensions (longitudinal length of hole Y₁ and hole-forming depth of cotton seed hole Y₂). Quadratic regression test using RecurDyn-EDEM coupling identified optimal parameter settings for maximum hole-forming performance. When A was 2.4°, B was 42.4 mm, and C was 30.5°, the performance of the hole-forming was the best. Under the optimal parameter combination, the bench verification test was carried out. The error between the bench verification results and the simulation results is small, indicating that the model has high accuracy. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill (0.11 s). It meets the requirements of high-speed cotton planting in China and facilitating advancements high-speed planter technology.

High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot’s moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856°.