Effect of uncertainty on continuous path planning for an autonomous vehicle

This paper describes one approach to the problem of path planning for an autonomous vehicle (an automaton) moving in two-dimensional space filled with obstacles. The approach is based on continuous processing of incoming local information about the environment. A continuous computational model for the environment and for the vehicle operation is presented. Information about the environment (the scene) is assumed to be incomplete except that at any moment the vehicle knows the coordinates of its target as well as its own coordinates. The vehicle is presented as a point; obstacles can be of any shape, with continuous borderline and finite size. Algorithms guaranteeing reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is evaluated in terms of perimeters of obstacles met by the vehicle. It is shown that with the exception of some rather unusual initial positions of the vehicle relative to the obstacles, one of the presented algorithms guarantees an optimal path.