In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future.
In the middle and late stages of maize, light is limited and non-maize obstacles exist. When a plant protection robot uses the traditional visual navigation method to obtain navigation information, some information will be missing. Therefore, this paper proposed a method using LiDAR (laser imaging, detection and ranging) point cloud data to supplement machine vision data for recognizing inter-row information in the middle and late stages of maize. Firstly, we improved the YOLOv5 (You Only Look Once, version 5) algorithm based on the characteristics of the actual maize inter-row environment in the middle and late stages by introducing MobileNetv2 and ECANet. Compared with that of YOLOv5, the frame rate of the improved YOLOv5 (Im-YOLOv5) increased by 17.91% and the weight size decreased by 55.56% when the average accuracy was reduced by only 0.35%, improving the detection performance and shortening the time of model reasoning. Secondly, we identified obstacles (such as stones and clods) between the rows using the LiDAR point cloud data to obtain auxiliary navigation information. Thirdly, the auxiliary navigation information was used to supplement the visual information, so that not only the recognition accuracy of the inter-row navigation information in the middle and late stages of maize was improved but also the basis of the stable and efficient operation of the inter-row plant protection robot was provided for these stages. The experimental results from a data acquisition robot equipped with a camera and a LiDAR sensor are presented to show the efficacy and remarkable performance of the proposed method.
Farmland irrigation is an essential foundation for good crop growth, while traditional farmland irrigation techniques cannot fully consider the impact of factors such as natural precipitation and crop transpiration on crop growth, which can, to a certain extent, result in poor irrigation decisions and a complex farmland environment that cannot be monitored promptly, thereby reducing farmland production efficiency. This study designs a farmland irrigation control system based on a composite controller. Firstly, an irrigation control method is proposed to establish a prediction model for future rainfall and crop transpiration using historical meteorological data. The composite controller is designed based on the prediction model to realize an irrigation control operation with an irrigation value as the control quantity, a water and fertilizer machine, and a solenoid valve as the actuators. Secondly, an intelligent irrigation control cloud platform based on Java language is designed to monitor farm information and irrigation operation records in real-time to facilitate visual management. Finally, the prediction accuracy is high, based on the prediction model results, which can provide a specific reference basis. The superiority of the proposed controller is verified by simulation using MATLAB/Simulink. The results show that the proposed controller can be well suited for nonlinear control systems and has good control performance while ensuring high tracking accuracy, strong robustness, and fast convergence.
In this paper, a new coupling simulation method is proposed for baler picker using automatic dynamic analysis of mechanical systems (ADAMS) and discrete element method (DEM). Field tests are carried out to verify the accuracy of the simulation model. By using the coupling method, not only was it obtained that the forward velocity (FV) and the ground clearance of spring teeth (GCST) are positively correlated with the pick-up loss rate (PLR), but also that the blockage of the picker mainly occurs in the straw pushing area, and an optimization plan is proposed. Through the analysis of the acting force (AF) between the roller and the track groove, we speculate that the structure of the track groove in a certain area is defective. The coupling method and optimization scheme proposed in this paper can provide a reference for the optimal design of the picker.
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