{'fraction_non_alphanumeric': 0.022388059701492536, 'fraction_numerical': 0.0, 'mean_word_length': 5.612676056338028, '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}}
Sensor-Based Recognition Fuzzy Mobile Robot Systems Designs

Evolutionary particle swarm optimization (PSO) machines with hybrid sensors (i.e., radio frequency identification (RFID) and E-compass wireless network sensors) and the concepts of traveling salesman problems (TSPs) are applied to recognize the optimal routing paths in a dynamic space. Four active RFID tags and an E-compass indicate the robot position. The PSO algorithm with the guides of specific fitness functions simultaneously develops the proper fuzzy rules, avoids obstacles, and approaches the shortest routing path. The practice mobile robot control problems are considered to demonstrate the efficiency of the fuzzy control system. An illustrated simulation shows that the mobile robot system with the evolutional learning scheme can acquire the appropriate fuzzy rules to overcome multiple obstacles and achieve desired goals in dynamic and nonlinear environments.