Goal-biased probabilistic foam method for robot path planning
Luís B. P. Nascimento, Diego S. Pereira, Pablo Javier Alsina, Maurício R. Silva, Daniel H. S. Fernandes, Válber César Cavalcanti Roza, Armando S. Sanca · 2018
This paper presents an improved variation of Probabilistic Foam Method (PFM) for robot path planning. In PFM, a structure named probabilistic foam, formed by bubbles propagate through the free space from initial configuration to goal as a breadth-first search, obtaining a collision-free path. Although the method is able to obtain a navigable path, it is computationally expensive. We propose a new foam propagation approach inspired on random tree growth from RRT. Results from simulation experiments using 2D and 3D map show benefits with the new method.