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Vision-based motion planning for an autonomous motorcycle on ill-structured roads

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Abstract

We report our development of a vision-based motion planning system for an autonomous motorcycle designed for desert terrain, where uniform road surface and lane markings are not present. The motion planning is based on a vision vector space (V2-Space), which is a unitary vector set that represents local collision-free directions in the image coordinate system. The V2-Space is constructed by extracting the vectors based on the similarity of adjacent pixels, which captures both the color information and the directional information from prior vehicle tire tracks and pedestrian footsteps. We report how the V2-Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how the V2-Space can be used to incorporate vehicle kinematic, dynamic, and time-delay constraints in motion planning to fit the highly dynamic requirements of the motorcycle. The combined algorithm of the V2-Space construction and the motion planning runs in O(n) time, where n is the number of pixels in the captured image. Experiments show that our algorithm outputs correct robot motion commands more than 90% of the time.

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Authors and Affiliations

  1. Department of Computer Science, Texas A&M University, College Station, TX, 77843, USA

    Dezhen Song

  2. Department of Electrical Engineering, Texas A&M University, College Station, TX, 77843, USA

    Hyun Nam Lee

  3. Department of Mechanical Engineering, San Diego State University, San Diego, CA, 92182, USA

    Jingang Yi

  4. Unmanned Systems Division, ENSCO Inc., Falls Church, VA, 22042, USA

    Anthony Levandowski

Authors
  1. Dezhen Song
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  2. Hyun Nam Lee
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  3. Jingang Yi
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  4. Anthony Levandowski
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Corresponding author

Correspondence to Dezhen Song.

Additional information

This work was supported in part by the National Science Foundation under IIS-0643298.

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Song, D., Lee, H.N., Yi, J. et al. Vision-based motion planning for an autonomous motorcycle on ill-structured roads. Auton Robot 23, 197–212 (2007). https://doi.org/10.1007/s10514-007-9042-y

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