Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs

Jonathan D. Gammell; Siddhartha S. Srinivasa; Timothy D. Barfoot

ICRA 2015poster612 citations

Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs

Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot

Abstract

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT).

BibTeX

@inproceedings{icra2015_batchinformedtre,
  title = {Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs},
  author = {Jonathan D. Gammell and Siddhartha S. Srinivasa and Timothy D. Barfoot},
  booktitle = {ICRA 2015},
  year = {2015}
}