Agricultural factories utilize advanced facilities and technologies to cultivate crops in a controlled environment, enhancing operational yields and reducing reliance on natural resources. This is crucial for ensuring a stable supply of agricultural products year-round and plays a significant role in the transformation of agricultural modernization. Automated Guided Vehicles (AGVs) are commonly employed in agricultural factories due to their low ownership costs and high efficiency. However, small embedded devices on AGVs face significant challenges in managing multiple tasks while maintaining the required timeliness. Multi-task learning (MTL) is increasingly employed to enhance the efficiency and performance of detection models in joint detection tasks, such as lane-line detection, pedestrian detection, and obstacle detection. The YOLOP (You Only Look for Panoptic Driving Perception) model demonstrates strong performance in simultaneously addressing these tasks; detecting lane lines in changeable agricultural factory scenarios is yet a challenging task, limiting the subsequent accurate planning and control of AGVs. This paper proposes a feedback-based network for joint detection tasks (MTNet) that simultaneously detects pedestrians, automated guided vehicles (AGVs), and QR codes, while also performing lane-line segmentation. This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. Homemade datasets were used for training and testing. Comparative experiments of our model with different models in the target-detection and lane-line detection tasks, respectively, show the progressiveness of our model. Surprisingly, we also obtained a significant improvement in the model’s processing speed. Furthermore, we conducted ablation experiments to assess the effectiveness of our improvements in lane-line detection, all of which outperformed the original detection model.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF-based tracking loops. First, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Second, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other tracking methods in complex dynamic environments.

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to estimate the initial state of the Kalman filter, the proposed method utilizes the designed quasi-uniform quaternion generation method to estimate several possible initial states. Then, the proposed method selects the most probable result based on the generalized Schweppe likelihood ratios among multiple hypotheses. The experiment result of the proposed method demonstrates the advantage of estimation performance within poor-quality measurement conditions for the long-duration coarse alignment using MEMS-IMU compared with the OBA-based method. The proposed method has potential applications in alignment tasks for low-cost, small-scale vehicle navigation systems.