Research interest in three-dimensional (3D) printing has been greatly aroused since 1990 due to its outstanding merits, such as freedom of design, mass customization, waste minimization and fast prototyping complex structures. To formally elaborate the research status of the 3D printing field, a bibliometric analysis is applied to evaluate the related publications from 1990 to 2020 based on the Science Citation Index Expanded database and Social Science Citation Index database. The overview with detailed discussions is cataloged by keywords, citation, h-index, year, journal, institution, country, author, patent and review. The statistical results show that the United States plays a dominant role in this research field, followed by China and the UK. Singapore is the most productive country with the highest average citations per publication (ACPP), and the second most cooperative country. Among all the institutions, Chinese Academy of Sciences is most productive, and Harvard University has the highest ACPP and h-index. Among all the journals, Materials ranks first in the number of publications in this field. The most attractive research area is “Materials science, Multidisciplinary”, with 4053 publications. Moreover, the major hot topics derived from authors’ keywords are “3D printing”, “additive manufacturing” and “tissue engineering”. Commercial and medical applications appear to be the initial driving force and end goal for the development of the 3D printing technology.

Grasping unstructured and fragile objects such as food and fruits is a great challenge for robots. Being naturally different from the traditional rigid robot, soft robotics provide highly promising choices with their intrinsic flexibility and compliance to objects. Inspired by duck foot and octopus tentacle, a pneumatic webbed soft gripper was proposed, which is consisted of four multi-chambered fingers and four webs. Due to its silicone body and soft web structure, the developed soft gripper can naturally adapt, grasp and hold delicate and unstructured objects. Compressed air inflated into the three chambers of the finger actuates the silicone body and performs inflection and extension. The silicone web follows the motion of four fingers, forming a semi-closed grasping configuration. The fingers were fabricated with silicone rubber and constraint spring by casting process. The web was cast around the fingers. The inflecting motion was modeled via the pneumatic principle and geometrical analysis. The dynamic properties of the finger were tested by step and sinusoidal signals. And the grasping performances for different objects, such as egg, strawberry, candy, and knife, were also demonstrated by experiments. The proposed soft gripper performed stably in response to a 0.4 Hz reference sinusoidal signal. The bionic structure greatly improves the stability and reliability of grasping, particularly for unstructured and fragile objects. Moreover, the webs ensure the grasping for multiple objects in one snatch, especially suitable for agricultural products and food processing. Keywords: soft gripper, biomimetics, grasping, robotic, pneumatic DOI: 10.25165/j.ijabe.20211404.6388 Citation: Cai S B, Tang C E, Pan L F, Bao G J, Bai W Y, Yang Q H. Pneumatic webbed soft gripper for unstructured grasping. Int J Agric & Biol Eng, 2021; 14(4): 145–151.

Rock drilling robots are able to greatly reduce labor intensity and improve efficiency and quality in tunnel construction. However, due to the characteristics of the heavy load, large span, and multi-joints of the robot manipulator, the errors are diverse and non-linear, which pose challenges to the intelligent and high-precision control of the robot manipulator. In order to enhance the control accuracy, a hybrid positional error compensation method based on Radial Basis Function Network (RBFN) and Light Gradient Boosting Decision Tree (LightGBM) is proposed for the rock drilling robot. Firstly, the kinematics model of the robotic manipulator is established by applying MDH. Then a parallel difference algorithm is designed to modify the kinematics parameters to compensate for the geometric error. Afterward, non-geometric errors are analyzed and compensated by applying RBFN and lightGBM including features and kinematics model. Finally, the experiments of the error compensation by combing combining the geometric and non-geometric errors verify the performance of the proposed method.