Approximation of the inverse kinematics of a robotic manipulator using a neural network

Abstract

A fundamental property of a robotic manipulator system is that it is capable of accurately following complex position trajectories in three-dimensional space. An essential component of the robotic control system is the solution of the inverse kinematics problem which allows determination of the joint angle trajectories from the desired trajectory in the Cartesian space. There are several traditional methods based on the known geometry of robotic manipulators to solve the inverse kinematics problem. These methods can become impractical in a robot-vision control system where the environmental parameters can alter. Artificial neural networks with their inherent learning ability can approximate the inverse kinematics function and do not require any knowledge of the manipulator geometry. This thesis concentrates on developing a practical solution using a radial basis function network to approximate the inverse kinematics of a robot manipulator. This approach is distinct from existing approaches as the centres of the hidden-layer units are regularly distributed in the workspace, constrained training data is used and the training phase is performed using either the strict interpolation or the least mean square algorithms. An online retraining approach is also proposed to modify the network function approximation to cope with the situation where the initial training and application environments are different. Simulation results for two and three-link manipulators verify the approach. A novel real-time visual measurement system, based on a video camera and image processing software, has been developed to measure the position of the robotic manipulator in the three-dimensional workspace. Practical experiments have been performed with a Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance of the radial basis function network is analysed for the manipulator operating in two and three-dimensional space and the practical results are compared to the simulation results. Advantages and disadvantages of the proposed approach are discussed