Work

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

Year: 2015

Type: conference-paper

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).

Source: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings

DOI: 10.1109/icra.2015.7139620

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

Institutions: University of Toronto, Carnegie Mellon University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Natural Sciences and Engineering Research Council of Canada, Ontario Ministry of Research and Innovation, Office of Naval Research

OpenAlex ID: W1814533834

FWCI: 25.65

Cites: 40

Cited by: 485

Related to: 10