With reference to the motion planning problem, we present a simple strategy for improving the connectivity of probabilistic roadmaps by genetic post-processing. In particular, our objective is to increase the roadmap density in narrow passages, where many of the existing probabilistic planners perform poorly. To this end, we associate to each individual (i.e., to each robot configuration) an easily computable fitness function based on the distance between disjoint components of the roadmaps. Straightforward selection, crossover and (possibly) mutation operators are then applied to improve the quality of the population. Numerical results in different workspaces, including a well-known benchmark, show the effectiveness of the proposed strategy -
Giuseppe, O., Panzieri, S., Andrea, T. (2006). Increasing the connectivity of probabilistic roadmaps via genetic post-processing. In Proc of 8th Int. IFAC Symp. On Robot Control SYROCO 2006. Bologna : -.
Increasing the connectivity of probabilistic roadmaps via genetic post-processing
PANZIERI, Stefano;
2006-01-01
Abstract
With reference to the motion planning problem, we present a simple strategy for improving the connectivity of probabilistic roadmaps by genetic post-processing. In particular, our objective is to increase the roadmap density in narrow passages, where many of the existing probabilistic planners perform poorly. To this end, we associate to each individual (i.e., to each robot configuration) an easily computable fitness function based on the distance between disjoint components of the roadmaps. Straightforward selection, crossover and (possibly) mutation operators are then applied to improve the quality of the population. Numerical results in different workspaces, including a well-known benchmark, show the effectiveness of the proposed strategy -I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.