期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
卷 37, 期 4, 页码 610-621出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2007.897499
关键词
dead cycle problem; mobile robot navigation; neurofuzzy; parameter tuning; redundant rule suppression
In this paper, a neurofuzzy-based approach is proposed, which coordinates the sensor information and robot motion together. A fuzzy logic system is designed with two basic behaviors, target seeking and obstacle avoidance. A learning algorithm based on neural network techniques is developed to tune the parameters of membership functions, which smooths the trajectory generated by the fuzzy logic system. Another learning algorithm is developed to suppress redundant rules in the designed rule base. A state memory strategy is proposed for resolving the dead cycle problem. Under the control of the proposed model, a mobile robot can adequately sense the environment around, autonomously avoid static and moving obstacles, and generate reasonable trajectories toward the target in various situations without suffering from the dead cycle problems. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
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