Dynamic anytime task and path planning for mobile robots

Research output: Contribution to conferencePaper

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.

Conference

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

Fingerprint

Motion planning
Mobile robots
Planning
Cost reduction

Keywords

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

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.
Wong, Cuebong ; Yang, Erfu ; Yan, Xiu-Tian ; Gu, Dongbing. / Dynamic anytime task and path planning for mobile robots. Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom.4 p.
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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.",
keywords = "robotics, autonomous systems, task planning, path planning, combined task and motion planning, dynamic planning",
author = "Cuebong Wong and Erfu Yang and Xiu-Tian Yan and Dongbing Gu",
year = "2019",
month = "1",
day = "16",
language = "English",
note = "The UKRAS19 Conference on Embedded Intelligence, UKRAS19 ; Conference date: 24-01-2019 Through 24-01-2019",
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Wong, C, Yang, E, Yan, X-T & Gu, D 2019, 'Dynamic anytime task and path planning for mobile robots' Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom, 24/01/19 - 24/01/19, .

Dynamic anytime task and path planning for mobile robots. / Wong, Cuebong; Yang, Erfu; Yan, Xiu-Tian; Gu, Dongbing.

2019. Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom.

Research output: Contribution to conferencePaper

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T1 - Dynamic anytime task and path planning for mobile robots

AU - Wong, Cuebong

AU - Yang, Erfu

AU - Yan, Xiu-Tian

AU - Gu, Dongbing

PY - 2019/1/16

Y1 - 2019/1/16

N2 - 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.

AB - 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.

KW - robotics

KW - autonomous systems

KW - task planning

KW - path planning

KW - combined task and motion planning

KW - dynamic planning

UR - https://www.ukras.org/news-and-events/uk-ras/

M3 - Paper

ER -

Wong C, Yang E, Yan X-T, Gu D. Dynamic anytime task and path planning for mobile robots. 2019. Paper presented at The UKRAS19 Conference on Embedded Intelligence, Loughborough, United Kingdom.