Dynamic anytime task and path planning for mobile robots

Cuebong Wong, Erfu Yang, Xiu-Tian Yan, Dongbing Gu

Research output: Contribution to conferencePaperpeer-review

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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


ConferenceThe UKRAS19 Conference on Embedded Intelligence
Abbreviated titleUKRAS19
Country/TerritoryUnited Kingdom
Internet address


  • robotics
  • autonomous systems
  • task planning
  • path planning
  • combined task and motion planning
  • dynamic planning
  • Best Paper Award

    Parsa, M. (Recipient), 2012

    Prize: Prize (including medals and awards)

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