Simulation of Object Recognition by a Robot

Machine vision is a useful robotic sensor since it mimics the human sense of vision and allows for non-contact measurement of the environment. A 3-D object gives rise to an infinite variety of 2D images or views, because of the infinite number of possible poses relative to the viewer. In order for a vision system to be effective in assisting a robot to approach an object autonomously, two things must be known—“what” the object is and “where” it is located ie. the object has to be recognised and its coordinates must be known. The system developed utilises two cameras for obtaining 3-D information about the object. A hierarchical system has been developed in software for object recognition. Training of each object is done by presenting characteristic views of each polyhedral object. Ideally, the vision system on the robot arm should be moved around the object to obtain the characteristic views. In the simulations however, the objects are rotated and displaced to mimic robot movement. Each image of an object is processed and features such as corners and edges are extracted. The relationships between the features are determined to identify the types of surfaces. The relationships between these surfaces is then encoded and input into the artificial neural network. Incremental learning is done using Fuzzy ARTMAP for all objects. When a single novel image of an object is presented, the correct object can be recognised. Since stereo vision is used, the location of the object with respect to the cameras is also determined. The vision system can now be implemented on a robot arm in the ‘eye-in-hand’ configuration. The robot arm can be moved precisely to obtain the two images or a miniature stereo setup can be mounted close to the gripper. This system can thus be used to identify, locate and approach mechanical objects autonomously.