A path planning algorithm for lane-following-based autonomous mobile robot navigation

Yazan Aljeroudi; Mark Paulik; Mohan Krishnan; Chaomin Luo

doi:10.1117/12.838926

18 January 2010 A path planning algorithm for lane-following-based autonomous mobile robot navigation

Yazan Aljeroudi, Mark Paulik, Mohan Krishnan, Chaomin Luo

Proceedings Volume 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques; 75390M (2010) https://doi.org/10.1117/12.838926
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

In this paper we address the problem of autonomous robot navigation in a "roadway" type environment, where the robot has to drive forward on a defined path that could be impeded by the presence of obstacles. The specific context is the Autonomous Challenge of the Intelligent Ground Vehicle Competition (www.igvc.org). The task of the path planner is to ensure that the robot follows the path without turning back, as can happen in switchbacks, and/or leaving the course, as can happen in dashed or single lane line situations. A multi-behavior path planning algorithm is proposed. The first behavior determines a goal using a center of gravity (CoG) computation from the results of image processing techniques designed to extract lane lines. The second behavior is based on developing a sense of the current "general direction" of the contours of the course. This is gauged based on the immediate path history of the robot. An adaptive-weight-based fusion of the two behaviors is used to generate the best overall direction. This multi-behavior path planning strategy has been evaluated successfully in a Player/Stage simulation environment and subsequently implemented in the 2009 IGVC. The details of our experience will be presented at the conference.

Citation Download Citation

Yazan Aljeroudi, Mark Paulik, Mohan Krishnan, and Chaomin Luo "A path planning algorithm for lane-following-based autonomous mobile robot navigation", Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 75390M (18 January 2010); https://doi.org/10.1117/12.838926

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