The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.

In this paper, we develop a low-cost delta robot arm for grasping objects of unspecified size thanks to a vision system. Stepper motors are used instead of ac servo motors to build a low-cost delta robot arm. Furthermore, we use available materials and machining methods such as laser cutting and 3d printing instead of CNC milling and turning to reduce fabrication costs. The controller is based on a low-cost embedded controller - Arduino Uno for controlling the robot's motion. The vision system is constructed to determine the 3D coordinate of objects in the workspace as well as the sizes of objects. The gripper is opened with a distance of two fingers equal to the size of the objects, and the robot is controlled to the objects' coordinates to grasp them. An application to pick up objects on a conveyor belt is developed to validate the design. The experimental results show that the robot system works correctly, the robot arm moves smoothly, and the information determined by the vision system has a small error, ensuring that the robot can accurately pick up products.

This paper develops a multi-robotic arm system and a stereo vision system to sort objects in the right position according to size and shape attributes. The robotic arm system consists of one master and three slave robots associated with three conveyor belts. Each robotic arm is controlled by a robot controller based on a microcontroller. A master controller is used for the vision system and communicating with slave robotic arms using the Modbus RTU protocol through an RS485 serial interface. The stereo vision system is built to determine the 3D coordinates of the object. Instead of rebuilding the entire disparity map, which is computationally expensive, the centroids of the objects in the two images are calculated to determine the depth value. After that, we can calculate the 3D coordinates of the object by using the formula of the pinhole camera model. Objects are picked up and placed on a conveyor branch according to their shape. The conveyor transports the object to the location of the slave robot. Based on the size attribute that the slave robot receives from the master, the object is picked and placed in the right position. Experiment results reveal the effectiveness of the system. The system can be used in industrial processes to reduce the required time and improve the performance of the production line.