This research paper presents the proposes a robotic vision system to distinguish the color for the object and his position coordinate, and then sort the object (product) on the right branch conveyor belt according to color in real-time. The system was built based on the HVS mode algorithm for sorting product based on color. Furthermore, the system can be distinguished the object shape and then find his position to picking the object shape and putting on the right branch conveyor belt. The assumptions for the object shape were based on the shape properties, centroid algorithm, and border extraction. Both the object detection and the contour coordinate extraction methods are implemented using a series of image processing techniques. The main goal is met by sorting the object depends on the color feature from a gathering of objects. The robot movement (open and close griper, move up and down the arm, and move to the left and right) controlled by a microcontroller that controls the movement to the right branch conveyor belt. When the color or the object is detected, the microcontroller will initiate the actions of the robot. It was found that the accuracy of results based on the approach that developed in this paper which is 92% for shape sorting and 97% for colors sorting objects.

A systematic approach is presented to achieve the stable grasping of objects through a two-finger robotic hand, in which each finger cavity was filled with granular media. The compaction of the latter, controlled by vacuum pressure, was used to adjust the structural and contact stiffness of the finger. The grasping stability was studied under the concurrent effect of an external torque and applied vacuum pressure. Stable grasping was defined as the no slippage condition between the grasped object and the two fingers. Three control schemes were adopted and applied experimentally to ensure the effectiveness of the grasping process. The results showed that stable and unstable grasping regions exist for each combination of applied torque and vacuum pressure. The two-finger robotic hands can be further improved for applications that require high load-carrying capabilities.

The present paper investigates a variable stiffness elastic actuator, which was adjusted using a special-purpose mechanism. A model was derived to obtain the response of the actuator. Concurrently, an experimental setup was designed and manufactured to validate the theoretical model. A novel control scheme is adopted to enhance the response through stiffness variation. Results showed that the time of response is reduced by 73% as compared to that of a constant stiffness actuator. In order to find the effectiveness of the proposed control scheme, a PID controller is implemented, where the response time was reduced by 58% using the former.

Many industrial and commercial applications today are beginning to use autonomous systems to increase productivity and reduce costs for production and manpower. Most of these applications are only semi- autonomous, it still needs assistance from a human to start up or receive continual instructions. It can be improve the productivity using the image processing techniques based on the camera processing capabilities and more efficient vehicles. This research paper describes a vision tracking system platform and USB camera that used to make a distinguishing operation applying to real-time video tracking processing for moving product. The industrial camera tracking system is designed to provide tracking and sorting for the products based on the shape quality criterion, which is means reject the product with low quality (bad shape). The platform is used to distinguish different shapes of products and tracking operation. The received video is displayed in the computer through the acquisition video, taking advantage of the toolkit for acquisition video and image processing. It can be determined where the place of the part and detect the product, and then send information to the control system to remove unwanted product.

Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the weld was completed according to the required working conditions and performance. The parts of the system work with compatible and consistent performances, with acceptable accuracy for tracking the line of the welding path.