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A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning

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Abstract

This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.

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Acknowledgments

This research was supported in part by the Air Force Research Laboratory, Munition Directorate under SBIR contract No. FA 8651-07-c-0094 to Scientific Systems Company, Inc. and Brigham Young University.

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Authors and Affiliations

  1. UtopiaCompression Corporation, 11150 W. Olympic Blvd, Suite 820, Los Angeles, CA, 90064, USA

    Huili Yu

  2. Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, 84602, USA

    Randy Beard

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  1. Huili Yu
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  2. Randy Beard
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Correspondence to Huili Yu.

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Yu, H., Beard, R. A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning. Auton Robot 34, 93–109 (2013). https://doi.org/10.1007/s10514-012-9314-z

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  • DOI: https://doi.org/10.1007/s10514-012-9314-z

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