Vision-based motion planning for an autonomous motorcycle on ill-structured roads
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 (V-2-Space), which is a unitary vector set that represents local collision-free directions in the image coordinate system. The V-2-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 V-2-Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how the V-2-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 V-2-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.
autonomous motorcycle vision-based navigation mobile robots