Integration Of The BUZZ Image Processing System With An Expert System For Vehicle Navigation
William G. Duggleby · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988
Modern manufacturing environments are using an increasing number of automated guided vehicles. While these robots can improve productivity, they are restricted to following preset paths marked by paint or electrical wires. A need exists for a robot cart which can determine its own path given knowledge of its surroundings and its goals. This type of robot could determine the shortest path to accomplish its goals, and it could use heuristic reasoning to determine how to get around obstacles which would normally block a painted path. Image processing techniques combined with knowledge-based reasoning can provide a solution to this problem. This paper discusses a system which acquires video images of a hallway, segments those images using image processing algorithms, and determines the classification of objects and the appropriate path for a cart using heuristic reasoning. The images are segmented using basic knowledge of the surroundings. Heuristics in the form of production rules are used to determine the classification for each segment and the corresponding confidence. The rules were developed from an analysis of the basic features common to object classes in the image database.