{'fraction_non_alphanumeric': 0.012422360248447204, 'fraction_numerical': 0.0, 'mean_word_length': 5.396825396825397, '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}}
Model-Based Coordination of Robotic Networks

The Problem: Robotic systems are being created that must act together to robustly achieve elaborate missions within uncertain and sometimes dangerous environments. To achieve this robustness we must go well beyond current programming practice. How do we program these teams of robots or vehicles to carry out elaborate missions, while offering them a breadth of options for dealing with the unknown? How will these robotic networks generate and adapt coordinated plans on the fly, while distributing the decision making amongst the robotic components in an effective manner? How will these robots best deal with the uncertainty of communication as well as uncertainty of the environment? How do we give these robots enough autonomy to perform agile maneuvers?