Real-time robot path planning around complex obstacle patterns through learning and transferring options

Olimpiya Saha, Prithviraj Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We consider the problem of path planning in an initially unknown environment where a robot does not have an a priori map of its environment but has access to prior information accumulated by itself from navigation in similar but not identical environments. To address the navigation problem, we propose a novel, machine learning-based algorithm called Semi-Markov Decision Process with Unawareness and Transfer (SMDPU-T) where a robot records a sequence of its actions around obstacles as action sequences called options which are then reused by it to learn suitable, collision-free maneuvers around more complex obstacles in future. Our results illustrate that SMDPU-T takes 24% planning time and 39% total time to solve same navigation tasks as compared to a recent, sampling-based path planner.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017
EditorsLino Marques, Alexandre Bernardino
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages278-283
Number of pages6
ISBN (Electronic)9781509062331
DOIs
StatePublished - Jun 29 2017
Event2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017 - Coimbra, Portugal
Duration: Apr 26 2017Apr 28 2017

Publication series

Name2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017

Other

Other2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017
CountryPortugal
CityCoimbra
Period4/26/174/28/17

ASJC Scopus subject areas

  • Biomedical Engineering
  • Control and Optimization
  • Mechanical Engineering
  • Artificial Intelligence

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  • Cite this

    Saha, O., & Dasgupta, P. (2017). Real-time robot path planning around complex obstacle patterns through learning and transferring options. In L. Marques, & A. Bernardino (Eds.), 2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017 (pp. 278-283). [7964088] (2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARSC.2017.7964088