{'fraction_non_alphanumeric': 0.013774104683195593, 'fraction_numerical': 0.0, 'mean_word_length': 5.228571428571429, '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}}
Vision and Motion Planning for a Mobile Robot under Uncertainty

This article describes a framework for vision and motion plan ning for a mobile robot. The robot's task is to reach the desti nation in the minimum time while detecting possible routes by vision. Since visual recognition is computationally expensive and the recognition result includes uncertainty, a trade-off must be considered between the cost of visual recognition and the effect of information to be obtained by recognition. Using a probabilistic model of the uncertainty of the recognition result, vision-motion planning is formulated as a recurrence formula. With this formulation, the optimal sequence of observation points is recursively determined. A generated plan is globally optimal, because the planner minimizes the total cost. An effi cient solution strategy is also described that employs a pruning method based on the lower bound of the total cost calculated by assuming perfect sensor information. Simulation results and experiments with an actual mobile robot demonstrate the feasibility of our approach.