An enhanced dynamic Delaunay Triangulation-based (DT) path planning approach is proposed for mobile robots
to plan and navigate a path successfully in the context of the Autonomous Challenge of the Intelligent Ground
Vehicle Competition (www.igvc.org). The Autonomous Challenge course requires the application of vision techniques
since it involves path-based navigation in the presence of a tightly clustered obstacle field. Course artifacts
such as switchbacks, ramps, dashed lane lines, trap etc. are present which could turn the robot around or cause
it to exit the lane. The main contribution of this work is a navigation scheme based on dynamic Delaunay
Triangulation (DDT) that is heuristically enhanced on the basis of a sense of general lane direction. The latter is
computed through a "GPS (Global Positioning System) tail" vector obtained from the immediate path history
of the robot. Using processed data from a LADAR, camera, compass and GPS unit, a composite local map
containing both obstacles and lane line segments is built up and Delaunay Triangulation is continuously run
to plan a path. This path is heuristically corrected, when necessary, by taking into account the "GPS tail"
. With the enhancement of the Delaunay Triangulation by using the "GPS tail", goal selection is successfully
achieved in a majority of situations. The robot appears to follow a very stable path while navigating through
switchbacks and dashed lane line situations. The proposed enhanced path planning and GPS tail technique has
been successfully demonstrated in a Player/Stage simulation environment. In addition, tests on an actual course
are very promising and reveal the potential for stable forward navigation.