With the development and upgrading of intelligent mines, research on the unmanned walking of intelligent electric shovels (ES) has been carried out to improve the moving efficiency of extra-large excavators. This paper first introduces an electric shovel’s primary moving condition in an open-pit mine. According to the moving characteristics of the heavy-duty crawler, the artificial potential field (APF) algorithm is improved to plan the moving trajectory of the electric shovel and carry out simulation verification. A dynamic model of an electric shovel is established. A fuzzy control tracking method is proposed based on preview displacement and centroid displacement deviation. The robustness of the tracking algorithm is verified by multi-condition simulation. Finally, the electric shovel prototype is tested through path planning and tracking experiments. The experimental results show that the improved artificial potential field algorithm can plan an obstacle-free path that satisfies the movement of an electric shovel, and the electric shovel can quickly track the preset trajectory. The maximum deviation of the track tracking center of mass is no more than 10 cm, and the deviation of the heading angle when the shovel reaches the endpoint is within 2°.

To improve the sowing performance of the spoon wheel maize seeding machinery, in this paper, two varieties of maize seed are selected as examples. The maize spoon precision seed metering device, a core component of the spoon wheel seeding machinery, is used as the research object. The maize seed assembly model is first established based on the maize seed assembly modeling method. Its validity is verified by the sowing experiment and corresponding DEM simulation under the different revolving speeds of the seed metering wheel. Secondly, the performance of the spoon precision seed metering device is optimized by integrating the maize seed assembly model and multivariate nonlinear regression method. Therefore, the number of sub-spheres of the horse tooth, spherical cone, and spheroid maize seed model are 10–14, 18, and 6, respectively. The results show that the performance of the seed metering device improved when the revolving speed of the seed metering wheel, handing angle, and seed spoon radius are 25 r/min, 40°, and 7 mm, respectively. There is good agreement between the expected results and experimental ones with relative errors of less than 5%, and the optimized seed metering device facilitates the process of seed guiding and seed delivery during the sowing process.

Soybean seeding monomers can realize the process of opening, seed throwing, covering, and compacting when they work. Due to the complexity of their working process, the relevant process cannot be analyzed by the discrete element method (DEM) alone. The DEM coupled with the multi-rigid body dynamics method (MBD) can solve the above problem, and the simulation analysis of the above process is realized by coupling the EDEM software with RecurDyn software. The changes in the position of soybean seed particles before and after covering and compacting are analyzed. The results show that when the working speed of the seeding monomer increases, the distance along the vertical direction of the soybean seed particles after covering gradually increases, and the distance along the horizontal direction gradually decreases. The effect of different working speeds of seeding monomer on the opening situation and the variation in seed particle positions is studied. The results show that the ditch angle gradually decreases as the working speed of the seeding monomer increases. The distribution of seed particle spacing is also analyzed. The above tests are simulated, and the results show a high agreement between the simulation and test results, proving the accuracy of the coupling method. This paper applies the coupling method for the first time to the simulation of the seeding monomer. This method can be applied not only to the analysis of the sowing process of soybean seeding monomers, but also be applied to the analysis of other machinery working processes, such as the tillage process, the sieving process, the planting and harvesting processes of crops, etc. It also deepens the application of the discrete element method in the field of agriculture.