Vision assisted measurement for optimization of robot motion and position control functions

This paper deals with a global vision based robotic control and measurement system. The two basic control functions that need to be implemented and tuned well are the functions to orient a robot towards a target and the function to position it at a point in the area of operation. Fast response necessitates high gain but at the risk of overshoots and unstable operation. Experimental data has been logged, using the global vision, and from the response characteristics the control parameters have been adapted for optimal performance. A PD control for robot position function is discussed. The vision processing is done in real-time, effectively within 16.67 ms sample time of an interlaced NTSC video image. Since odd and even fields are processed separately, there is inherent quantization noise in the system. This can make the system unstable when the gain is high. Kalman filtering has been introduced with good results to reduce noise and improve predictive control. Performance of the system with different filter parameters has been evaluated.