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Adaptively Informed Trees (AIT*): fast asymptotically optimal path planning through adaptive heuristics

Alternative title:
Conference paper
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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA40945.2020.9197338

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1034-3889


Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2020 IEEE International Conference on Robotics and Automation (ICRA)
Pages:
3191-3198
Publication date:
2020-09-15
Acceptance date:
2020-01-31
Event title:
ICRA 2020: International Conference on Robotics and Automation
Event location:
Virtual
Event website:
http://icra2020.org/
Event start date:
2020-05-31
Event end date:
2020-06-04
DOI:
EISSN:
2577-087X
ISSN:
1050-4729
EISBN:
9781728173955
ISBN:
9781728173962


Language:
English
Keywords:
Pubs id:
1090542
Local pid:
pubs:1090542
Deposit date:
2020-03-02

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