{'fraction_non_alphanumeric': 0.0225, 'fraction_numerical': 0.0, 'mean_word_length': 4.977611940298507, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Robot path generation for surface processing applications via neural networks

This paper presents a hierarchical method, based on a deterministic variant of the self-organizing map, that provides an elegant solution for automated surface processing, e.g. for robot painting and sand-blasting. Given a set of data points in arbitrary order from the object surface, the proposed method is able to generate a path, where the robot hand position and its direction are optimized using separate criteria, and the tool path is smooth and covers the object uniformly. Input data may come from a laser measurement system, CAD model, digital camera, or from human assisted object digitizing system. The algorithm is reliable and easy to implement, and a good alternative for costly manual training of a robot.