Agricultural robots have been increasingly recognized to improve agricultural productivity and labor saving for smart farming in modern agriculture. Compared with the structured industrial environments, the agricultural working scenarios are characterized by strong openness, high dynamics, and pronounced unstructured properties. There are great variations in the crop morphology and growth stages, complex and uneven terrain conditions, as well as multiple sources of environmental uncertainty, such as the illumination and weather fluctuations. It is significantly difficult for robotic perception, decision-making, and control. As a result, the efficiency and scalability of the agricultural robots can often severely suffer from the long experimental cycles, high costs of field trials, limited availability of large-scale real-world data, and insufficient repeatability of algorithm validation. The virtual simulation can be expected for the agricultural robotics research, testing, and deployment, due to its advantages in the safety, cost controllability, and experimental repeatability. A controllable and reproducible environment of the robotic systems can be designed and then evaluated under diverse operating conditions without the expenses of real-world agricultural experiments. The computing power, physical modeling, and graphics rendering have further promoted the high-fidelity simulation in recent years. In this study, a systematic review was presented on the virtual simulation technologies. The evolution history was traced from early applications in the training simulators to modern high-fidelity and multi-modal systems. Furthermore, the application trajectory of the virtual simulation was reviewed in the robotics field, in order to integrate computer graphics, physical modeling, artificial intelligence, and interactive technologies. Particularly, the application directions of the virtual simulation were summarized in agricultural robotics, including unstructured agricultural environments, kinematic and dynamic analysis of the robotic systems, the operational workflows and task strategies, safety evaluation of the human-robot collaboration, and operator skill training. Representative scenarios of the agricultural operation were also analyzed using mainstream simulation platforms, such as Gazebo, Unity3D, and NVIDIA Isaac Sim. These platforms were then evaluated at different stages of the agricultural robots, in terms of the physical simulation accuracy, visual realism, interaction, and integration with the robotic middleware systems. Typical agricultural tasks were examined, such as orchard harvesting, crop protection, pruning, and seeding. Application examples were also given on the domain randomization, computer vision, autonomous navigation, and procedural content generation within the virtual simulation environments. The simulation was then reduced to the real-world experimental risks in order to accelerate the algorithm iteration and efficiency. As such, several key challenges were proposed in the virtual simulation of agricultural robotics. For instance, there were some difficulties in the environment modeling and task adaptability, due to the complexity and variability of the agricultural scenes. Technical limitations and application bottlenecks of the simulation reduced the modeling accuracy of the physical interactions and contact dynamics between robots and agricultural objects, such as the crops and soil. Moreover, the gap between simulation and actual robotic performance was also attributed to the discrepancies between the simulated and real-world sensors under environmental conditions. Finally, the future trends of the virtual simulation were predicted in agricultural robotics, including the high-fidelity, multi-scale, and multi-modal simulation, the integration with artificial intelligence, digital twin technologies, and data-driven modeling. The virtual simulation can be expected to play an increasingly important role in the research tasks of the agricultural robots, such as algorithm validation, system evaluation, and methodological comparison. Overall, this review can provide a strong reference to further develop the virtual simulation in agricultural robotics for smart farming.

Agricultural robot is one of the hottest topics in the field of agricultural machineries. Domestic and foreign research on robot mobile platforms for greenhouse/farmland/orchard operations（weeding，fertilization，spraying，picking，etc.）has achieved preliminary results，but mobile robotic system for soil sampling is still seldom found in literatures. Undisturbed soil sampling is an important basis for analyzing soil mechanical properties. If the original state of the soil sample cannot be guaranteed，it will be difficult to obtain accurate research results through subsequent laboratory physical and mechanical testing and analysis. To address this issue，we developed a farmland soil collection robot mobile platform with compact structure，strong pass ability，good soil extraction quality，and high soil extraction efficiency，put forward the design scheme of the mechanical system and control system and carried out preliminary field trial research. The main contents of this research are as follows：First，the design of robotic mobile platform was conducted，by determining differential steering mode，fulfilling mechanical design，selecting hardware components，and building control software framework. The wheelbase and track width of the platform were 960 mm and 600 mm，respectively. The power of the in-wheel motor was 1000 W. The movement of the platform could be controlled through both speed control knob and remote-control handle. Second，an on-board layer soil sampling equipment was developed，which worked in a hydraulic screw-in mode. The main structure parameters of the sampler were determined based on theoretical analysis，which were validated with a finite element analysis software-ANSYS. Third，field tests were also conducted to test the mobility and soil sampling performances of the robot system. The maximum obstacle crossing height and climbing slope of the robot were 80 mm and 35°，respectively. Based on the shear strength testing results of soil samples in depth 0 to 200 mm，we knew that the internal friction angle of the soil samples，which came from the proposed new system，had no significant differences compared to those coming from cutting ring sampling，with a P-value of 0.866 at the confidence level of 0.05. Similarly，for the soil samples in depth 0 to 100 mm and 100 to 200 mm，the variances of soil cohesion from our new system also had no significant differences compared to those from cutting ring sampling，with P-values of 0.145 and 0.717 at the confidence level of 0.05，respectively. The soil extraction efficiency comparison test results showed that the soil extraction device only took 3 to 5 minutes to complete one soil extraction.