<title>Adaptive semi-autonomous robotic neurocontroller</title>

Abstract

We have designed a neural network semiautonomous robotic arm controller. This controller performs end-effector path planning, inverse kinematics, and joint control to move the end- effector to a commanded position. We have tested the adaptive neural joint controller and inverse kinematics in simulation. The joint controller has been tested on two real arms. These real arms are the Extendable Stiff Arm Manipulator (ESAM) and the Proto-Flight Manipulator Arm (PFMA). Both of these arms are very different, yet the same unmodified joint controller software can control them both. The controller has also shown tremendous adaptability to large payload variations. It has been shown to adapt to a 35 pound end-effector payload on the ESAM from a zeroed initial state. This ability to handle different arms and payloads is due to the fact that the controller makes no assumptions as to the arm's dynamics or payload. The same tests performed on a decentralized PD controller showed that the neural network controller is superior.