Comparison of polymers: a new application of shape from focus
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Mobile robot behavior coordination using supervisory control of fuzzy discrete event systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Human tracking from a mobile agent: Optical flow and Kalman filter arbitration
Image Communication
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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.