{'fraction_non_alphanumeric': 0.02666666666666667, 'fraction_numerical': 0.0, 'mean_word_length': 5.296774193548387, '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}}
Chemical Plume Tracing and Mapping via Swarm Robots

This chapter addresses the key issues of chemical plume mapping and tracing via swarm robots. First, the authors present the models of turbulent odor plumes with both non-buoyant and buoyant features, which can efficiently evaluate strategies for tracing plumes, identifying their sources in two or three-dimensions. Second, the authors use the Monte Carlo technique to optimize moth-inspired plume tracing via swarm robots under formation control, which includes a leader to perform plume tracing maneuvers and non-leaders to follow the leader during plume tracing missions. Third, the authors introduce a variety of robot-based plume tracers, including ground-based robots, autonomous underwater vehicles, or unmanned aerial vehicles. Finally, the authors prospect the further research in this area, e.g., applying swarm robots to detect oil or gas leak, or to investigate subsea chemical pollution and greenhouse gases.