Abstract

To implement an autonomous mobile robot, both SLAM and task based navigation algorithms should be performed successfully. Especially, the performance of the estimation while the mobile robot performs a task based navigation should be guaranteed. For this purpose, we integrate a SLAM method and a navigation algorithm for practical autonomous mobile robot. The SLAM method combines sonar sensors and stereo camera together using the EKF-based SLAM. Fusing sonar features and visual objects can give correct data association via object recognition and high frequency update via sonar features. The navigation algorithm consists of global and local path planner when the goal position is given. The global path planner uses modified A* algorithm and it gives the mobile robot enough opportunity to detect the registered landmarks during moving to the goal position. As a local path planner, for safe obstacle avoidance, we propose Circle Following (CF) algorithm. The performance of the proposed algorithm was verified by experiments in home environment with dynamic obstacles.

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