Pan Jiluan

Research on a visual weld detection method based on invariant moment features

A visual weld trace detection method based on invariant moment features is proposed in this paper for cosmetic welding inspection. The proposed method can not only overcome the absence of distortion in structural light detection, but also solve the problems of existing detection method based on grayscale weld image such as many priori parameters needed, time-consuming feature extraction process, instable features etc. This research shows that the proposed detection method has advantages in better adaptability, fewer priori parameters and higher efficiency. The detection error of the proposed method is not more than 0.3 mm. The seam and base metal region can be classified effectively. The proposed method is expected to be applied to real-time seam tracking in multi-layer welding.

Position and Pose Detection of Moving Object in Fog and Dust Environment Based on Laser Array

A position and pose detection method of moving object in fog and dust environment is proposed in this paper. Laser array comprised of four angled lasers is mounted on a turntable fixated on the detecting object. The turntable is tuned automatically to guarantee that all laser spots are projected onto a plate, which keeps stationary with the world coordinate system. The position and pose of detecting object can be figured out according to laser spot positions. The position precision of the proposed method can reach 0.2% or higher in the detecting range of 25-30m. The azimuth detection range can be as wide as 0-360° with the tuning of the turntable. Simulations are carried out to evaluate the influence of the measurement errors on detecting results, revealing that the detection accuracy does not exceed 9mm and 0.01rad in the permitted noise levels. Those results indicate that the proposed method is expected to be applied to high-precision position and pose detection of moving objects (mobile robots, special machinery, etc.) in fog and dust environment.