The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the harvesting robot are the perception module and the end-effector. Intel RealSense D435i is adopted to collect RGB images and point cloud images in real time; an improved SSD algorithm is proposed to detect mushrooms, and finally, the existing soft gripper is manipulated to grasp oyster mushrooms. Field experiments exhibit the feasibility and robustness of the proposed robot system, in which the success rate of the mushroom recognition success rate reaches 95%, the harvesting success rate reaches 86.8% (without considering mushroom damage), and the harvesting time for a single mushroom is 8.85 s.

For automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper, a method is proposed for peduncle cutting point localization and pose estimation. Images captured in real time at a fixed long-distance are detected using the YOLOv4-Tiny detector with a precision of 92.7% and a detection speed of 0.0091 s per frame, then the YOLACT + + Network with mAP of 73.1 and a time speed of 0.109 s per frame is used to segment the close-up distance. The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.

Over the past decade, there have been increasing attempts to integrate robotic harvesting technology into agricultural scenarios to reduce growing labour costs and increase crop yields. In this paper, we demonstrate a prototype harvesting robot for picking watermelons in greenhouses. For robotic harvesting, we design a dedicated end-effector for grasping fruits and shearing pedicels, which mainly consists of a flexible gripper and a cutting device. The improved YOLOv5s–CBAM is employed to locate the watermelon fruits with 89.8% accuracy on the test dataset, while the K-means method is used to further refine the segmentation of the watermelon point cloud in the region of interest. Then, the ellipsoid is fitted with the segmented fruit point cloud to obtain the lowest point of the ellipsoid as the grasping point. A series of tests conducted in a laboratory simulation scenario proved that the overall harvesting success rate was 93.3% with a positioning error of 8.7 mm when the watermelon was unobstructed. The overall harvesting success rate was 85.0% with a positioning error of 14.6 mm when the watermelon was partially obscured by leaves.

Nano-positioning technology has been widely used in many fields, such as microelectronics, optical engineering, and micro manufacturing. This paper presents a one-dimensional (1D) nano-positioning system, adopting a piezoelectric ceramic (PZT) actuator and a multi-objective topological optimal structure. The combination of a nano-positioning stage and a feedback capacitive comb sensor has been achieved. In order to obtain better performance, a wedge-shaped structure is used to apply the precise pre-tension for the piezoelectric ceramics. Through finite element analysis and experimental verification, better static performance and smaller kinetic coupling are achieved. The output displacement of the system achieves a long-stroke of up to 14.7 μm and high-resolution of less than 3 nm. It provides a flexible and efficient way in the design and optimization of the nano-positioning system.