Kalman-Filter-Based Machine Vision for Controlling Free-Flying Unmanned Remote Vehicles

Automatic control of a robotic vehicle requires a navigation system to determine the vehicle´s position and motion at each sampling interval for feedback corrections to be made. Human beings largely depend on vision for their own navigation, and it provides high-quality navigation data in a wide variety of environments. Machine-based vision systems have generally been too computationally expensive and slow, however, for use in real-time control systems. The system provided here achieves the required speed for real-time control through use of simple geometric models of the perceived target, dependence on tracking rather than object recognition, and reduction of the scene analysis task from a two-dimensional process to a set of one-dimensional scans through the image. The system is intended for application to a neutrally-buoyant vehicle called STAR that simulates a freely-flying, extravehicular space robot. The vision system will support development of autonomous and teleoperator control technologies for space robots, and the experimental results presented here result from preliminary target-pointing experiments with the STAR vehicle.