Robotics Arm Visual Servo: Estimation of Arm-Space Kinematics Relations with Epipolar Geometry

Abstract

Numerous advances in robotics have been inspired by reliable concepts of biological systems. Necessity for improvements has been recognized due to lack of sensory capabilities in robotic systems which make them unable to cope with challenges such as anonymous and changing workspace, undefined location, calibration errors, and different alternating concepts. Visual servoing aims to control a robotics system through artificial vision, in a way as to manipulate an environment, in a similar way to humans actions. It has always been found that, it is not a straightforward task to combine "Visual Information" with a "Arm Dynamic" controllers. This is due to different natures of descriptions which defines "Physical Parameters" within an arm controller loop. Studies have also revealed an option of using a trainable system for learning some complicated kinematics relating object features to robotics arm joint space. To achieve visual tracking, visual servoing and control, for accurate manipulation objectives without losing it from a robotics system, it is essential to relate a number of an object's geometrical features (object space) into a robotics system joint space (arm joint space). An object visual data, an play important role in such sense. Most robotics visual servo systems rely on object "features Jacobian", in addition to its inverse Jacobian. Object visual features inverse Jacobian is not easily put together and computed, hence to use such relation in a visual loops. A neural system have been used to approximate such relations, hence avoiding computing object's feature inverse Jacobian, even at singular Jacobian postures. Within this chapter, we shall be discussing and presenting an integration approach that combines "Visual Feedback" sensory data with a "6-DOF robotics Arm Controller". Visual servo is considered as a methodology to control movements of a robotics system using certain visual information to achieve a task. Visionary data is acquired from a camera that is mounted directly on a robot manipulator or on a mobile robot, in which case, motion of the robot induces camera motion. Differently, the camera can be fixed, so that can observe the robot motion. In this sense, visual servo control relies on techniques from image processing, computer vision control theory, kinematics, dynamic and real time computing.