Abstract—This paper concerns with real-time local obstacle avoidance for mobile robots. We use probabilistic clearance approach to avoid obstacles efficiently and for sharper turning and more time for the navigator to detect additional nearby obstacles. A combination of probabilistic clearance matrix with a Gaussian weighing is used to overcome sudden increase in probabilistic clearance value, which is mainly because of errors in the reading due to low-cost sensors such as the ultrasonic proximity sensors, thus making the algorithm suitable for developing low cost navigators as well. Before taking any decision for movement, the algorithm takes into consideration the nearby environment also for better results. The algorithm is designed in such a way that the navigator chooses a path having less number of obstacles thus reducing the probability of collision to a very high extent.
The Algorithm has been successfully implemented and extensively tested on an autonomous robot at Delhi College of Engineering, India.
Index Terms—Gaussian, Probabilistic Clearance, Navigator, Obstacle Avoidance
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Cite: Puneet Kumar, Nikhil Jindal, Akhil Jindal and Sidharth Chhabra, "A Robust Algorithm for Local Obstacle Avoidance,"
International Journal of Computer Theory and Engineering vol. 2, no. 3, pp. 401-404, 2010.
