Sampling-based A* algorithm for robot path-planning

This paper presents a generalization of the classic A* algorithm to the domain of sampling-based motion planning. The root assumptions of the A* algorithm are examined and reformulated in a manner that enables a direct use of the search strategy as the driving force behind the generation of new samples in a motion graph. Formal analysis is presented to show probabilistic completeness and convergence of the method. This leads to a highly exploitative method which does not sacrifice entropy. Many improvements are presented to this versatile method, most notably, an optimal connection strategy, a bias towards the goal region via an Anytime A* heuristic, and balancing of exploration and exploitation on a simulated annealing schedule. Empirical results are presented to assess the proposed method both qualitatively and quantitatively in the context of high-dimensional planning problems. The potential of the proposed methods is apparent, both in terms of reliability and quality of solutions found.