Abstract
An enhanced obstacle avoidance algorithm for a network-based autonomous mobile robot is proposed in this paper. Firstly, the readings of the environmental sensors at a moment are compensated to the prospecting readings of the sensors considering network delay measured and the kinematic model of the robot. The compensated readings of the sensors are used for building the polar histogram of the VFH algorithm. Secondly, a sensory fusion using the Kalman filter is proposed for the localization of the robot to compensate both the delay of the readings of an odometry sensor and the delay of the readings of the environmental sensors. The performance enhancements of the proposed obstacle avoidance algorithm from the viewpoint of efficient path generation and accurate goal positioning are also shown in this paper through some simulation tests.
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