{'fraction_non_alphanumeric': 0.014583333333333334, 'fraction_numerical': 0.0010416666666666667, 'mean_word_length': 4.652941176470589, '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}}
Dynamic model based ball trajectory prediction for a robot ping-pong player

In this paper, a novel ball trajectory prediction method is proposed for a robot ping-pong player. The method is based on the dynamic model in the sky and the bouncing dynamic model between the ball and the table. First, the ambiguity of the dynamic model in the sky is removed. Then, the bouncing model between the ball and the table is proposed to get the relationship of the emergence speed and the incidence speed. The corresponding coefficients of models are trained according to the 3D Cartesian coordinates of the ball calculated from a high speed stereo vision system. With known dynamic models, the trajectory can be predicted if several initial trajectory points of the ball from the vision system are given. Experiments show that the trajectory is able to be predicted accurately in several milliseconds. The result of our method is better than that by LWR based approach.