Merging the adaptive random walks planner with the randomized potential field planner

In this paper we investigate whether it is advantageous to merge some ideas formerly found in the randomized potential field planner with our recently introduced adaptive random walks planner. These aspects are biasing the generation of samples, an attractor for the samples generator, and the possibility to backtrack when the planner gets stuck while exploring the configuration space. We illustrate the numerical results of different experiments using these strategies one at the time, or combined together. It turns out that benefits of different amplitude can be obtained using them, but it is in general hard to incorporate these components in a general way independent from the problem instance to be solved.