Packaging is one of the least automated steps among all the fruit postharvest processes, which is time-consuming and labor-intensive. Therefore, a robust suction strategy for robotic manipulation needs to be developed. In this research, a geometric-based apple suction strategy for robotic packaging was studied, including suction cup design, optimal suction region selection algorithm, and robot system integration. In the first place, on the basis of the geometric features of the spheroid fruit, the structure of the suction cups was designed to provide reliable suction force. Then, suction force measurement experiments on both acrylic balls and apples were conducted. Based on the results, the parameters of the suction cup were finally determined. The results also indicated that the curvature radius of the suction region is supposed to larger than that of the suction cups. Furthermore, a robust suction region selection algorithm was developed, which involves four steps: RGB-D information acquisition, object detection and point cloud generation, spherical fitting, and suction region selection. Finally, the above methods were integrated into a robotic packaging system. In addition, on the basis of spatial-frequency domain imaging (SFDI) technology, early stage bruise was detected for validation. The results showed that, the proposed suction strategy and system is potential for robust robotic apple packaging.

Manipulating cells at a small scale is widely acknowledged as a complex and challenging task, especially when it comes to cell grasping and transportation. Various precise methods have been developed to remotely control the movement of microrobots. However, the manipulation of micro-objects necessitates the use of end-effectors. This paper presents a study on the control of movement and grasping operations of a magnetic microrobot, utilizing only 3 pairs of electromagnetic coils. A specially designed microgripper is employed on the microrobot for efficient cell grasping and transportation. To ensure precise grasping, a bending deformation model of the microgripper is formulated and subsequently validated. To achieve precise and reliable transportation of cells to specific positions, an approach that combines an extended Kalman filter with a model predictive control method is adopted to accomplish the trajectory tracking task. Through experiments, we observe that by applying the proposed control strategy, the mean absolute error of path tracking is found to be less than 0.155 mm. Remarkably, this value accounts for only 1.55% of the microrobot’s size, demonstrating the efficacy and accuracy of our control strategy. Furthermore, an experiment involving the grasping and transportation of a zebrafish embryonic cell (diameter: 800 μm) is successfully conducted. The results of this experiment not only validate the precision and effectiveness of the proposed microrobot and its associated models but also highlight its tremendous potential for cell manipulation in vitro and in vivo.