Identification of the end-effector positioning errors of a high accuracy large medical robot using neural networks

In this paper, the problem of achieving high accuracy positioning of a medical robot is studied. The Northeast Proton Therapy Center, at the Massachusetts General Hospital, is a new cancer research and treatment facility. A major component of the center is a robotic patient positioning system that will carry and position patients in a proton beam. The desired positioning accuracy of the robot is less than 0.5 mm. However, various sources of errors in the robot such as assembly errors or flexible deformation of the robot links result in big end-effector positioning errors. It is important to know these end-effector errors as a function of the robot joint variables and patient weight, to be able to compensate them using the manipulator controller. A multi-layer neural network is proposed to identify the robot positioning errors. The neural network is trained using the Levenberg-Marquardt method. Simulations and experimental results demonstrate the validity of the neural network.