Autonomous vehicle navigation and mapping with local minima avoidance paradigm in unknown environments

Abstract

A commonly used local navigation algorithm, the Vector Field Histogram (VFH), is relatively fast and thus suitable when computational capabilities on a robot are limited. One of the attendant disadvantages of this algorithm is that a robot can get trapped when attempting to get past a concave obstacle structure. The Navigation Challenge course now has several such structures, including some that partially surround waypoints. Elaborate heuristics are needed to make VFH viable in such a situation and their tuning is arduous. An alternate approach that avoids the use of heuristics is to combine a dynamic path planning algorithm with VFH. In this paper, the D*Lite path planning algorithm is used to provide VFH with intermediate goals, which the latter then uses as stepping stones to its final destination. Results from simulation studies as well as field deployment are used to illustrate the benefits of using the local navigator in conjunction with a path planner.