{'fraction_non_alphanumeric': 0.01937269372693727, 'fraction_numerical': 0.0, 'mean_word_length': 5.235632183908046, '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}}
Robust and Efficient Multi-robot Coverage

Area coverage is an important task for mobile robots, with many real-world applications. Motivated by potential efficiency and robustness improvements, there is growing interest in the use of multiple robots in coverage. Previous investigations of multi-robot coverage algorithms focused on the improved efficiency gained from the use of multiple robots, but did not formally addressed the potential for greater robustness. We address robustness and efficiency in a family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition. We present off-line and on-line algorithms and analytically show that the algorithms are complete and robust, in that as long as a single robot is able to move, the coverage will be completed. We analyze the assumptions underlying the algorithms requirements and present a number of techniques for executing it in real robots. We show extensive empirical coverage-time results of running the algorithms in a number of different environments and several group sizes.