Collision- and freezing-free navigation in dynamic environments using Learning to Search

Chung Che Yu*, Chieh-Chih Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

While collision-free navigation could be done using existing rule-based approaches, it becomes more attractive to use learning from demonstration (LfD) approaches to ease the burden of tedious rule designing and parameter tuning procedures. In addition, in the freezing robot problem, once the environment surpasses a certain level of complexity, there may be no sufficient space for a robot to navigate using these planning or navigation approaches even with perfect predictions of moving entities. In this paper, it is argued that collision-free navigation in dynamic environments is learnable from demonstrations with proper feature sets without the use of a path planner. It is feasible to solve the freezing robot problem using the policies learned from demonstration. The simulation results demonstrate that the Learning to Search (LEARCH) approach with the proposed modification is capable of achieving collision- and freezing-free navigation in dynamic environments.

Original languageEnglish
Title of host publicationProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
Pages151-156
Number of pages6
DOIs
StatePublished - 1 Dec 2012
Event2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
Duration: 16 Nov 201218 Nov 2012

Publication series

NameProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

Conference

Conference2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
CountryTaiwan
CityTainan
Period16/11/1218/11/12

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