In this paper, we present a recent survey on robotic grippers. In many cases, modern grippers outperform their older counterparts which are now stronger, more repeatable, and faster. Technological advancements have also attributed to the development of gripping various objects. This includes soft fabrics, microelectromechanical systems, and synthetic sheets. In addition, newer materials are being used to improve functionality of grippers, which include piezoelectric, shape memory alloys, smart fluids, carbon fiber, and many more. This paper covers the very first robotic gripper to the newest developments in grasping methods. Unlike other survey papers, we focus on the applications of robotic grippers in industrial, medical, for fragile objects and soft fabrics grippers. We report on new advancements on grasping mechanisms and discuss their behavior for different purposes. Finally, we present the future trends of grippers in terms of flexibility and performance and their vital applications in emerging areas of robotic surgery, industrial assembly, space exploration, and micromanipulation. These advancements will provide a future outlook on the new trends in robotic grippers.

This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.