Labor shortages and the development of digital technology both impose requirements on the fruit industry. Modern agricultural competition has shifted from competition between products to competition between supply chains. Enhancing the digitization of production lines is crucial for gaining a competitive advantage. Strawberries, as fruits with a short shelf life, require sorting and packaging of fruits of different weights after being harvested. Estimating strawberry weight through visual technology can save time and labor costs. Common methods include methods based on feature size and learning-based methods, with the former having larger errors and the latter requiring a large amount of data. To address these issues, we propose a dataset for estimating strawberry weight, which includes strawberries with different heights and angles. Additionally, we propose a strawberry weight estimation method based on plane-constrained binary division point cloud completion. This method separates the plane point cloud and strawberry point cloud, constructs a coordinate system on the strawberry point cloud, generates an axis-aligned bounding box (AABB), and estimates the strawberry weight based on the bounding box and placement plane as constraints. Through comparison with different methods, we achieved a maximum improvement of 20.95% in prediction accuracy, demonstrating that our method provides the best estimation accuracy.

As a classical and crucial component in industrial systems, the manipulators are widely employed in precision manufacturing scenarios because of their advantages of high stiffness, large load support capability, and high precision. During their service, it is inevitable that they encounter data imbalance scenarios due to the occasional and low-frequency failure behaviors. But in order to address these issues, the majority of the approaches already in use need the assistance of extra tools. Thus, a novel intelligent health state diagnosis model, named multiple neighbor homogeneous property-embedded graph auto-encoder (MNHP-GAE), is developed to get around this restriction and apply it to the manipulators. Its core is to realize the expansion and enrichment of the feature space by mining effective complementary information from homogeneous property samples without the assistance of data augmentation and other technologies. Specifically, the wavelet decomposition reconstruction and dynamic time warping are integrated to promote the quantification of the sample similarity and enable the construction of homogeneous property graph samples. Following that, a unique graph auto-encoder module with the multi-head attention mechanism is constructed to extract complementary information from homogeneous property nodes and match it for diagnostic tasks. Finally, through a multi-case experimental validation scenario constructed by a 3-PRR planar parallel manipulator experimental platform, the superior performances of the proposed MNHP-GAE model in highly unbalanced scenarios are fully demonstrated.

Alignment between the injection pipette and the holding pipette/biological entity is crucial for successful cell microinjection. Precision alignment can reduce cell damage and improve cell microinjection. However, in the traditional microscopic vision-based method, lack of depth perception is a drawback in monocular vision. On the other hand, stereo vision with a limited baseline distance and field of view (FOV) makes it challenging to observe biological samples. In this article, a novel hybrid triple-view micro-vision system, in which a binocular telecentric micro-vision unit and an inverted microscope are combined for scene depth and detail sense, is presented for cell microinjection. Based on this system, coarse-to-fine feature extraction methods for two discrepant pipettes are proposed, and a two-stage needle alignment strategy is established based on hybrid pose/image servo control. Some feature extraction and alignment experiments are implemented, and the results demonstrate that the proposed method achieves satisfactory alignment performance (mean position deviation: about 0.1$\mu$m; mean orientation deviation: about 0.4$^\circ$) for zebra embryo microinjection, and the defined symmetry measure can reach 0.09.

Model-based control can provide high-accuracy performance over position-based or velocity-based control. Therefore, to employ model-based control in industrial robots, it is important to estimate the dynamic parameters as accurately as possible. However, traditional estimation methods, such as least squares (LS), are not sufficiently accurate, and the feasibility of dynamic parameters cannot be guaranteed. In this article, an iterative hybrid least square (IHLS) algorithm is proposed to estimate the base parameters of industrial robots by dividing the identification processes into two loops. The inner loop integrates a linear matrix inequality with the semidefinite programming technique to guarantee physical feasibility and reuses the torque deviations between the measured torque and predicted torque to estimate the base parameters of the robot, while the outer loop substitutes the Stribeck friction model for the Coulomb-viscous friction model to estimate the joint friction torque. Moreover, backpropagation neural network (BPNN) is introduced to further estimate the joint friction torque based on the Stribeck friction model. Experiments are conducted on two industrial robots, and four methods are compared in dynamic parameter identification. Experimental results show that the hybrid approach of the IHLS algorithm with the BPNN has the best performance among the four methods.

Lightly-damped dynamics of a flexure-based mechanism will tend to largely deteriorate the broadband control performance if its hysteresis nonlinearity has been compensated. This paper developed a novel damped piezo-driven decoupled XYZ nanopositioning stage, which consists of three orthogonal parallel kinematic subchains, in which each subchain has a translational pair using a bridge-type piezo-driven actuator and two Cardan joints using two orthogonal- axis flexure hinges. Especially, we add damped inserts into each Cardan joint, as their shearing damping effects can enhance joint damping. Kinematic coupling is theoretically modeled and the decoupled dynamic model of the stage is built up. The frequency response functions with no-damping and damping scenarios are obtained by finite element transient analysis and experimental modal analysis respectively. The results indicate that the damping inserts embedded in the flexure hinges can enhance the stiffness and damping simultaneously, which will benefit broadband motion control of the nanopositioning stage.