Robot Vision is one of the most important applications in Image processing. Visual interaction with the environment is a much better way for the robot to gather information and react more intelligently to the variations of the parameters in that environment. A common example of an application that depends on robot vision is that of Pick-And-Place objects by a robotic arm. This work presents a method for identifying an object in a scene and determines its orientation. The method presented enables the robot to choose the best-suited pair of points on the object at which the two-finger gripper can successfully pick the object. The scene is taken by a camera attached to the arm’s end effector which gives 2D images for analysis. The edge detection operation was used to extract a 2D edge image for all the objects in the scene to reduce the time needed for processing. The methods proposed showed accurate object identification which enabled the robotic to successfully identify and pick an object of interest in the scene.

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Obstacle avoidance is an important concept to be considered when a robotic system is installed in an environment. Obstacles within the working space of a robotic system can prevent the robot from performing the tasks assigned to it properly; hence, the designer of the robotic system must program the robot to follow an emergency strategy that enables it to avoid any contact with a probable obstacle anywhere anytime within the working space of the robot. For mobile robots this task is relatively easier to accomplish since this type of robots have the flexibility to change their routes and take alternatives that are free of obstacles. For fixed robotic arm manipulators the situation is much more complex. In this paper we are only concerned with obstacle avoidance techniques and present a real-time obstacle avoidance method for fixed robotic arms that combines the benefits of both global and local techniques in that it can be used for both static and dynamic environments with a reduced computational effort.