In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

A large amount of data acquisition and annotation work is required to train a supervised machine learning model for open scenes. However, traditional manual approaches are inefficient. Here, a method is proposed for on-site image acquisition and semiautomatic annotation based on eye-tracking. This method uses the recognition capabilities and computational advantages of humans and machines to improve annotation efficiency, overcoming the bottleneck of AI-based approaches to the natural scenery understanding of field robots. The proposed method contains three advancements. First, we designed a head-mounted display with a built-in pose measurement module to achieve first-person teleoperation data acquisition, where a pseudoframe interpolation algorithm is designed to overcome the latency problem in immersive remote data transmission and to achieve efficient field data acquisition. Second, we propose an adaptive superpixel segmentation algorithm to reduce human–machine interactions based on eye-tracking. Third, since traditionally, the annotation process cannot provide feedback to the acquisition process and results in a low conversion rate. We proposed a new conversion rate index denoting the rate of transforming collected data into valid data to quantify the acquisition quality in real time. While achieving an annotation quality of 0.964 per the Dice index, which is approximately equal to that of the manual method, the proposed method improves the annotation efficiency by more than 3 times. Finally, the agricultural field experiments containing a real-life scene of robotic tomato-picking verified that the proposed method based on human–computer interaction can make full use of human perception and recognition intelligence.

Electrostatic abrasive implantation technology is a classical process based on electrostatic field to implant abrasive particles into base material. However, there is still not a quantitative model to ensure the implantation performance due to the fact that the electrostatic abrasive implantation is a complex multi-physics coupled process. To ensure the quality of sandpaper and elucidate the complex motion mechanism of Al₂O₃ based abrasive particles in a high voltage electrostatic field, a multi-physical field coupling simulation model is proposed. First, the mechanical model is constructed for the complex problem of the electrostatic abrasive implantation process. Then, the field model is established for the problem that the superposition of multi-physical fields leads to complicated environmental conditions. Finally, the evaluation model is established for the problems that the abrasive planting effect is difficult to evaluate and the planting parameters are difficult to adjust. Besides, a tailored electrostatic abrasive planting testing equipment is designed for the implantation performance analysis purpose. Single-parameter electrostatic abrasive planting experiments were conducted to analyze and verify the effect of different pole plate voltage and pole plate spacing on the abrasive implantation rate. To ensure the implantation performance, the applicable pole plate spacing ranges from 30 mm to 50 mm and the suitable voltage is 20–40 kV according to the simulation result in the proposed two models. Meanwhile, the implantation rate experimentation shows the coupling field model coincides with the experiments better. The key factors affecting the electrostatic abrasive planting process are identified, and a feasible multi-physical field coupled abrasive particle motion simulation model is proposed.