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Estimation, Search, and Planning (ESP) Research Group

Adaptively Informed Trees (ICRA 2020)

Marlin Strub also had his paper on the Adaptively Informed Trees (AIT*) planning algorithm accepted to ICRA 2020. This is the second of two papers he will be presenting in Paris and you can already read it on arXiv and get more details about the code.

Authors
  1. M. P. Strub
  2. J. D. Gammell
Title
Adaptively Informed Trees (AIT*): Fast asymptotically optimal path planning through adaptive heuristics
Publication
Conference
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
Pages
3191–3198
Date
Code
Code
Videos
Video
Presentations
Presentation
PDFs
PDF
Digital Object Identifier (DOI)
doi: 10.1109/ICRA40945.2020.9197338
Google Scholar
Google Scholar

Abstract

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic.

Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial.

This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.