Dynamic anytime task and path planning for mobile robots

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Abstract

The study of combined task and motion planning has mostly been concerned with feasibility planning for high-dimensional, complex manipulation problems. Instead this paper gives its attention to optimal planning for low-dimensional planning problems and introduces the dynamic, anytime task and path planner for mobile robots. The proposed approach adopts a multi-tree extension of the T-RRT* algorithm in the path planning layer and further introduces dynamic and anytime planning components to enable low-level path correction and high-level re-planning capabilities when operating in dynamic or partially-known environments. Evaluation of the planner against existing methods show cost reductions of solution plans while remaining computationally efficient, and simulated deployment of the planner validates the effectiveness of the dynamic, anytime behavior of the proposed approach.
Original languageEnglish
Number of pages4
Publication statusAccepted/In press - 16 Jan 2019
EventThe UKRAS19 Conference on Embedded Intelligence - Loughborough University, Loughborough, United Kingdom
Duration: 24 Jan 201924 Jan 2019
https://www.ukras.org/news-and-events/uk-ras/ukras19-about/

Conference

ConferenceThe UKRAS19 Conference on Embedded Intelligence
Abbreviated titleUKRAS19
CountryUnited Kingdom
CityLoughborough
Period24/01/1924/01/19
Internet address

Keywords

  • robotics
  • autonomous systems
  • task planning
  • path planning
  • combined task and motion planning
  • dynamic planning

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  • Prizes

    Best Paper Award

    Mahdi Parsa (Recipient), 2012

    Prize: Prize (including medals and awards)

    Cite this

    Wong, C., Yang, E., Yan, X-T., & Gu, D. (Accepted/In press). Dynamic anytime task and path planning for mobile robots. Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom.