The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight YOLOv11 (the eleventh version of You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed using the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. The experimental results demonstrate that the method proposed in this paper can accurately identify blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a maximum positioning error of 7.2 mm within 0.5 m, and an average 3D pose error of 19.2 degrees (around 10 degrees at the ideal picking angle) while maintaining a detection frame rate of 13.4 FPS (frames per second) on the OAK depth camera, providing effective picking guidance for the mechanical arm of picking robots.

The heat pump system employed with a dual evaporator for battery cooling coupled with cabin comfort is an innovative thermal management method. It can be inferred that the refrigerant thermal load distribution can trigger temperature fluctuations for thermal performance of both battery and cabin. To trade-off between the thermal management demands of battery and cabin, this study proposed a strategy to promote the decreasing of battery temperature and to ensure battery thermal uniformity with a higher priority. Hence a transient refrigerant flow rate distribution scheme with a minimum flow rate to satisfy battery thermal demands was designed. The results showed a significant reduction in the temperature fluctuations for cabin thermal comfort and BTM thermal controlling. It offers a satisfactory reference for refrigerant thermal load distribution strategy applied with the heat pump system connected to the battery and cabin by the dual evaporator.

Infrasound leakage signals, with low propagation energy loss, are ideal for long-distance and small leakage detection but suffer severe background noise interference. Existing wavelet denoising methods using traditional soft/hard threshold functions face critical limitations: soft thresholds introduce constant deviation, while hard thresholds cause discontinuities, both leading to suboptimal noise reduction for infrasound signals—this gap hinders accurate leakage detection. To address this, we propose a wavelet denoising method with an improved threshold function, analyze its process via the Mallat algorithm, and prove its continuity and convergence. Comparative experiments on infrasound leakage data show that, at the optimal decomposition level, our method reduces RMSE by 41.19% and increases SNR by 5.1326 dB compared to the soft threshold method; versus the hard threshold method, RMSE decreases by 34.65% and SNR increases by 4.2148 dB. It also separates background noise more thoroughly in time–frequency domains, demonstrating strong feasibility for pipeline infrasound leakage detection.

Automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance of blueberry fruits, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight yolov11 (the eleventh version of the You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed onto the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. Experimental results demonstrate that the method proposed in this paper can accurately perceive blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a positioning error of less than 1 centimeter within 0.5 meters, and an average 3D pose error of 19.2 degrees while maintaining the detection frame rate of 13.4 FPS (Frames Per Second) on the OAK depth camera, which can provide effective picking guidance for the mechanical arm of picking robot.