Abstract
In this paper, firstly, a model for robot navigation in unknown environment is presented as a Simulink model. This model is applicable for obstacles avoidance during the robot navigation. However, the first model is unable to recognize the re-occurrences of the obstacles during the navigation. Secondly, an advanced algorithm, based on the standard deviations of laser scan range vectors, is proposed and implemented for robot navigation. The standard deviations of the lasers scans, robot positions and the time of scans with similar standard deviations are used to obtain the obstacle recognition feature. In addition to the obstacle avoidance, the second algorithm recognizes the re-appearances of the obstacles in the navigation path. Further, the obstacle recognition feature is used to break the repetitive path loop in the robot navigation. The experimental work is carried out on the simulated Turtlebot robot model using the Gazebo simulator.
Highlights
Autonomous navigation of robots is gaining popularity among the researchers day by day
A model for robot navigation in unknown environment is presented as a Simulink model
The robot may trap in a repetitive navigation path loop in some situations
Summary
Autonomous navigation of robots is gaining popularity among the researchers day by day. Dynamic colour perception model for the autonomous navigation is explained in [2]. This model is not suitable for the laser range finder sensors. An algorithm for obstacles avoidance in unknown environment using bumper events of the robot is proposed in [7]. A fuzzy controller for obstacle avoidance in unknown environment is presented in [8]. In this paper, obstacle avoidance with obstacle recognition, to break the repetitive path loop during robot navigation in unknown and dynamic environment, is presented.
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