Optimization design and experiment on feeding and chopping device of silage maize harvester

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Design and experiment analysis of the small maize harvester with attitude adjustment in the hilly and mountainous areas of China

The driving speed of autonomous agricultural vehicles is influenced by surrounding cooperative vehicles during cooperative operations, leading to challenges in simultaneously optimizing operational efficiency, energy consumption, safety, and driving smoothness. This bottleneck hinders the development of autonomous cooperative systems. To address this, we propose a hierarchical speed decision control framework. The speed decision layer employs a maximum entropy-constrained proximal policy optimization (DMEPPO) reinforcement learning method, incorporating operational efficiency, energy consumption, safety, and smoothness as reward metrics to determine the optimal speed target. The speed control layer utilizes a Linear Matrix Inequality (LMI)-based robust control method for precise speed tracking. The experimental results demonstrate that the proposed DMEPPO achieved convergence after 2000 iterations and better learning performance, while the LMI-based controller achieved robust and responsive tracking. This architecture provides a theoretical foundation for speed decision control in agricultural vehicle cooperation scenarios. By considering aspects of speed decision-making control such as energy conservation, good solutions can be provided for the sustainable development of agriculture.