Position control of a robotic manipulator using a Radial Basis Function Network and a simple vision system

This paper describes a new practical approach for approximating the inverse kinematics of a manipulator using a RBFN (radial basis function network). In fact, there are several traditional methods based on the known geometry of the manipulator to calculate the relationship between the joint variable space and the world coordinate space. However, these traditional methods are impractical if the manipulator geometry cannot be easily determined, in a robot-vision system for example. Therefore, a neural network with its inherent learning ability can be an effective alternative solution for the inverse kinematics problem. In this paper, a training approach using the strict interpolation method combined with the LMS (least mean square) is presented. The strict interpolation method with regularly spaced position training patterns in the workspace can produce an appropriate approximation of the inverse kinematic function. Additionally, the LMS algorithm can improve the approximate function iteratively through on-line training with arbitrary position patterns. The combination of these techniques can deal with variation in the set-up of the visual system used to measure the position of the manipulator in the workspace. To verify the performance of the proposed approach, a practical experiment has been performed using a Mitsubishi PA10-6CE manipulator observed by a webcam. All application programmes, such as robot servo control, neural network, and image processing tool, were written in C/C++ and run in a real robotic system. The experimental results prove that the proposed approach is effective.