# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 7 Conclusions  
In this study, a new type of adaptive gripper was proposed, analyzed, and validated through analytical and experimental approaches. Each finger in the proposed adaptive gripper comprised a series of four-bar linkage structures, offering compliance as well as adaptability when adapting to an object's surface with redundant degrees of freedom in the model gripper. The proposed adaptive robotic gripper consisted of three identical fingers, each of which was composed of three four-bar linkage structures that can be independently controlled to change their orientations. With its adaptive features and three-finger structure, the proposed adaptive gripper demonstrated the feasibility of stable and reliable grasping of objects of various shapes.  
This study also implemented an efficient method for camera recognition, positioning, and tracking control using an eye-in-hand robotic configuration. The main advantages of eye-in-hand configuration include occlusion avoidance, intuitive teleoperation, imaging at various angles, easier calibration, and high accuracy. Furthermore, the proposed eye-in-hand robotic gripper featured a modular design and did not require an extra computer, power, or Ethernet cables to communicate with the Raspberry PI3 controller and robotic articulator such as UR5. All these features make it convenient for real-life application scenarios. In addition, an object tracking system was established to allow the underlying robotic manipulator to carry the proposed gripper to track objects on a constant-speed conveyor.  
The first contribution of this research was the establishment of a mathematical kinematic model of four-bar linkages in the design of adaptive fingers. Using this analytical model, the position, velocity, and torque transmission of the adaptive robotic finger can be derived. Second, a prototypical adaptive three-finger robotic gripper designed using the derived analytical model was developed, and its specifications were validated through quantitative analytical predictions and qualitative experimental observations. Finally, an eye-in-hand gripper prototype was developed by using an eye-in-hand sensor, allowing the gripper to easily track target objects and execute appropriate grasping actions. By using a general RGB camera with the depth calculation feature, it provided an approximate 4 mm standard deviation in depth error, which was similar to the depth accuracy of an Intel  
Figure 28: Illustrative examples showing the model gripper grasping (**a**) a small cylinder, and (**b**) an eyeglass box object from the conveyor.  
Realsense RGB-D camera. On the other hand, the gripper achieved a 100% grasping success rate when the conveyor speed was lower than 70 mm/s with different objects.  
Conceptualization, L.-W.C., S.-W.L. and J.-Y.C.; methodology, L.-W.C.; software, L.-W.C. and S.-W.L.; validation, L.-W.C. and S.-W.L.; formal analysis, L.-W.C.; investigation, L.-W.C.; data curation, L.-W.C. and S.-W.L.; writing--original draft preparation, L.-W.C.; writing--review and editing, J.-Y.C.; visualization, L.-W.C.; supervision, J.-Y.C.; project administration, L.-W.C.; funding acquisition, J.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology (MOST) of Taiwan through grant number 110-2218-E-007-054.  
Not applicable.  
The authors declare no conflict of interest.