Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Abstract

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 27, 2020
Source ID
10.1177/0278364919890396

Entities

People

  • Jonathan D. Gammell
  • Siddhartha Srinivasa
  • Timothy D. Barfoot

Organizations

  • Ministry of Research and Innovation
  • Natural Sciences and Engineering Research Council
  • Office of Naval Research
  • University of Oxford
  • University of Toronto
  • University of Washington

Tags

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy