Study On Robot Vision System Of 220kV/330kV High-Voltage Live-Line Cleaning Robot

This dissertation presents a research on several important problems of robot vision system of a 220kV/330kV high-voltage live-line cleaning robot (HLCR). HLCR is a semi-automatic system to clean insulators in outdoor 220kV/330kV high-voltage transformer substation. In order to implement automatic operation, a robot vision system will be applied to estimate the related posture between insulator and robot. The main research is as follows:Presenting two solutions on robot vision system Based on an analysis of HLCR, two solutions of robot vision system were given: eyes-on-hand robot vision system and remote robot vision system. Several problems coming from above solutions had been studied, which included: 1.How to represent invariant features of insulators? 2. How to recognize and localize insulators? 3. How to estimate the motion of a 3D rigid marker?Presenting a SUSAN edge-based scale invariant feature(SESIF) Being the weak textured object with curved smooth surface and locating in outdoor environment, a representation of insulator must consider some factors, for example: changed on illumination, camera parameters, viewpoint, cluttered background, material and texture of object. In this dissertation, we presented a SUSAN edge-based scale invariant feature (SESIF) to describe insulators, which was derived from SUSAN principle, discrete scale-space theory and local feature descriptor. The algorithm of SESIF was given and a series of invariant experiments of invariant including comparison with other invariant features on viewpoint, scale and rotation, image blur, noise, JPEG compression and illumination was done too.Recognizing and localizing weak textured object using SESIF Firstly, matching and constraints on SESIF were approached, as follows: 1.SNN algorithm was used to find matches, but k-d tree. Our experiments proved that SNN is less expense than k-d tree with a suitable boundary condition; 2.A peak number constraint was presented to eliminate incorrect matches; 3.General Hough transform was applied to eliminate more than 50% incorrect matches. Secondly, A recognition and accurate localization method was described, which include of 1.A coarse recognition and localization with RANSAC method; 2.A accurate localization with a Lie group based tracker. Our experiment proved that above algorithm was effective. Tracking 3D rigid marker on robot using SESIF We had extended Lie group based tracking algorithm with edge features to with SESIF. New algorithm solved several problems of original one: 1.With a match set found by SNN, no edge losing happened; 2.Being invariant on illumination with SESIF; 3.The initial estimation can been given by recognition and localization using SESIF; 4.An iterative estimation is used to localize object accurately with less expense; 5.A distance constraint was applied to eliminate the most incorrect match for frames with narrow baseline. Several related experimental results were presented.In conclusion, a research on robot vision system of HLCR was given: a novel SESIF was presented and evaluated to describe, recognize, localize insulators and a 3D rigid marker, and two solutions were introduced in details.