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.

Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.

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