Abstract

To automate nondestructive inspections of aircraft structures, a robot has been built equipped with jointed legs and suction cup feet that allow it to maneuver over the skinned surface of an aircraft exterior. ROSTAM (Robotic System for Total Aircraft Maintenance) has two prismatic degrees of freedom, two revolute degrees of freedom, and sensors for leg (suction cup) contact with the climbing surface. ROSTAM was designed with a sequential controller that runs on a computer attached via an umbilical cord. The sequential controller manipulates each joint separately in a distinct sequence of operations which moves the robot across the surface while maintaining adequate suction attachment during the process. To enhance the motion of the robot, a feed-forward neural network controller is trained to copy the control provided by the current sequential controller. The neural network takes as inputs, the same state and sensor information and outputs the same sequence of control actions as the sequential controller. This network controller is the first step in a plan to design a neural controller that can learn a viable walking procedure without copying an existing controller.

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