This paper presents a method for automatic sensor placement for model-based robot vision. In such a vision system, the sensor often needs to be moved from one pose to another around the object to observe all features of interest. This allows multiple three-dimensional (3-D) images to be taken from different vantage viewpoints. The task involves determination of the optimal sensor placements and a shortest path through these viewpoints. During the sensor planning, object features are resampled as individual points attached with surface normals. The optimal sensor placement graph is achieved by a genetic algorithm in which a min-max criterion is used for the evaluation. A shortest path is determined by Christofides algorithm. A Viewpoint Planner is developed to generate the sensor placement plan. It includes many functions, such as 3-D animation of the object geometry, sensor specification, initialization of the viewpoint number and their distribution, viewpoint evolution, shortest path computation, scene simulation of a specific viewpoint, parameter amendment. Experiments are also carried out on a real robot vision system to demonstrate the effectiveness of the proposed method. Index Terms-Christofides algorithm, hierarchical genetic algorithm, robot vision, sensor placement, viewpoints.
REF propose automatic sensor placement by a genetic algorithm for model-based robot vision.
Automatic sensor placement for model-based robot vision
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Computer Science Medicine