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In this paper, a navigation system is developed. The system includes path tracking and obstacle avoidance apparatus for a car-like wheeled robot (CLWR) within an Internet-based smart-space (IBSS) using fuzzy-neural adaptive control (FNAC). Two distributed charge-coupled device (CCD) cameras are installed to capture both the dynamic pose of the CLWR and the obstacle. Based on the control authority of these two CCD cameras, a suitable reference command that contains the desired steering angle and angular velocity for the FNAC built into the client computer is planned. Because of the delay encountered by the transmission through the Internet network (IN) and the wireless local area network (WLAN) and the nonlinear coupling features of the CLWR, a weighted combination of N linear subsystems that are described by a state-space model with average-delay is implemented to approximate the dynamics of an IBSS-CLWR. The proposed FNAC contains a neural network consisting of a radial basis function (RBFNN) to learn the uncertainties due to the fuzzy-model error (e.g., the random time-varying delays and the slippage of the CLWR) and the interactions caused by other subsystems. The stability of the overall system is then investigated by adopting the Lyapunov stability theory. Finally, a sequence of experiments including the control of the off-ground CLWR (i.e., the CLWR does not make contact with the ground) and the navigation of the IBSS-CLWR as compared with the conventional proportional-integral-derivative (PID) control is performed to demonstrate the advantage of the proposed control system.