The continuous development of smart agriculture puts forward the requirement of high accuracy slope path tracking for the agricultural wheel-legged robot. Compared to flat terrain, path tracking control on sloped terrain faces the obstacle of motion instability of the wheel-legged robot induced by the slope gravitational force component, which causes instantaneous steering center to offset. To address this problem, this study proposed a slope path tracking control algorithm by combining the methods of virtual sensing radar and two-level neural network. Firstly, the kinematic and dynamic models of the wheel-legged robot are deduced, from which the crucial factors affecting control accuracy of slope path tracking are recognized. Secondly, this study constructs the slope path tracking control algorithm, in which the virtual sensing radar is utilized to realize route perception, and the two-level neural network is employed to provide drive motors’ speeds to adapt to path tracking on different slopes. Furthermore, the corresponding compensation methods of the identified impacting factors are embedded in the proposed algorithm, including the lateral tracking deviation factor, heading angle deviation factor, slope change factor, and slip rate factor. Finally, the co-simulation model of slope path tracking control is constructed, including the multi-body dynamic model of the wheel-legged robot in RecurDyn and the proposed slope path tracking algorithm complied by Python. Subsequently, the simulation tests of the wheel-legged robot are carried out under various slope angles and velocities. The results reveal that the proposed algorithm’s effectiveness and accuracy are superior, with tracking errors reduced by more than 47.2% compared to an optimized pure pursuit algorithm.

When the silage harvester operates in the field, the filling and throwing system has a great influence on the efficiency of the silage harvester. Aiming at the feeding process of silage harvester, this study designs a precise throwing system for silage machine based on rotating target detection in order to realize the adaptive throwing of silage crumbs to the wagon compartment. In this study, a filling rule is proposed through the definition of ideal drop point and actual drop point. At the same time, an improved YOLOv5s (Rotation-YOLOv5, R-YOLOv5) was proposed to further realize the accurate detection of rotating targets using machine vision. The feedback of the target and the actual falling point were then calculated to control the movement of the throwing cylinder. The self-adaptive control was achieved in the throwing cylinder using R-YOLOv5. The network structure of the baseline was also optimized to predict the rotating carriages. A new prediction channel of rotation angle was then added to the Head part of the original YOLOv5. The virtual environment was built using Python3.9. Pytorch1.6 was chosen to train the R-YOLOv5 rotating target detection model on the Pytorch deep learning framework. Once the number of training rounds reached 60, the mAP values were all stable at 0.97, indicating a stable system. The improved R-YOLOv5 was recognized as a rectangle with a rotation angle. The recognition range was synchronized with the angle of the carriages. The training data was highly compatible with the carriages for better recognition. In addition, the throwing control was set under the distance between the actual and target drop position. A rotating carriage identification and a field test were carried out to develop the control flow. The test results show that the improved R-YOLOv5 was better performed on the rotating compartments at different rotating angles with an average of more than 97% before, indicating better detection; When the projectile barrel was rotated in the range of -60°−60°, angular velocity was less than 15°/s. The average accuracy of R-YOLOv5 target detection was higher than 90%; better recognition was achieved in the high accuracy of the actual drop position output, where more material was outflowed in unit time. The material outflow of the throwing cylinder in unit time was mainly related to the cutter's rotational and harvesting walking speed in the silage harvester. The correct rate of 85% or more was obtained to fully meet the harvesting requirements at the cutter rotational speed of 1 200 r/min, forward speed of 6 km/h, and the amount of material out of the unit between the Ⅳ level or more; The visual information was inputted by Arduino controller along with R-YOLOv5. The motion of the throwing cylinder was realized with 0.533 cm/pt, as the ratio of the actual distance to the image pixels. The average error between the theoretical and actual motion trajectory of the throwing cylinder was kept within 4%, which fully met the design requirements. The finding can provide a strong reference to develop the adaptive throwing-filling system in the self-propelled silage harvester.

In response to the weaknesses of traditional agricultural equipment chassis with poor environmental adaptability and inferior mobility, a novel unmanned agricultural machinery chassis has been developed that can operate stably and efficiently under various complex terrain conditions. Initially, a new wheel-legged structure was designed by drawing inspiration from the motion principles of locust hind legs and combining them with pneumatic-hydraulic linkage mechanisms. Kinematic analysis was conducted on this wheel-legged configuration by utilizing the D-H parameter method, which revealed that its end effector has a travel range of 0-450 mm in the X-direction, 0-840 mm in the Y-direction, and 0-770 mm in the Z-direction, thereby providing the structural foundation for features such as independent four-wheel steering, adjustable wheel track, automatic vehicle body elevation adjustment, and maintaining a level body posture on different slopes. Subsequently, theoretical analysis and structural parameter calculations were completed to design each subsystem of the unmanned chassis. Further, kinematic analysis of the wheel-legged unmanned chassis was carried out using RecurDyn, which substantiated the feasibility of achieving functions like slope leveling and autonomous obstacle negotiation. An omnidirectional leveling control system was also established, taking into account factors such as pitch angle, roll angle, virtual leg deployment, and center of gravity height. Joint simulations using Adams and Matlab were performed on the wheel-legged unmanned chassis, comparing its leveling performance with that of a PID control system. The results indicated that the maximum absolute value of leveling error was 1.08° for the pitch angle and 1.19° for the roll angle, while the standard deviations were 0.21647° for the pitch angle and 0.17622° for the roll angle, demonstrating that the wheel-legged unmanned chassis surpassed the PID control system in leveling performance, thus validating the correctness and feasibility of its full-directional body posture leveling control in complex environments. Finally, the wheel-legged unmanned chassis was fabricated, assembled, and subjected to in-place leveling and ground clearance adjustment tests. The experimental outcomes showed that the vehicle was capable of achieving in-place leveling with response speed and leveling accuracy meeting practical operational requirements under the action of the posture control system. Moreover, the adjustable ground clearance proved sufficient to meet the demands of actual obstacle crossing scenarios.