Optimal path planning based on a multi-tree T-RRT* approach for robotic task planning in continuous cost spaces

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Abstract

This paper presents an integrated approach to robotic task planning in continuous cost spaces. This consists of a low-level path planning phase and a high-level Planning Domain Definition Language (PDDL)-based task planning phase. The path planner is based on a multi-tree implementation of the optimal Transition-based Rapidly-exploring Random Tree (T-RRT*) that searches the environment for paths between all pairs of configuration waypoints. A method for shortcutting paths based on cost function is also presented. The resulting minimized path costs are then passed to a PDDL planner to solve the high-level task planning problem while optimizing the overall cost of the solution plan. This approach is demonstrated on two scenarios consisting of different cost functions: obstacle clearance in a cluttered environment and elevation in a mountain environment. Preliminary results suggest that significant improvements to path quality can be achieved without significant increase to computation time when compared with a T-RRT-based implementation.
Original languageEnglish
Pages242-247
Number of pages6
DOIs
Publication statusPublished - 10 Sep 2018
Event12th France - Japan Congress, 10th Europe - Asia Congress on Mechatronics - Mie University, Tsu, Mie, Japan
Duration: 10 Sep 201812 Sep 2018
http://www.tc-iaip.org/mecatronics2018/index.html

Conference

Conference12th France - Japan Congress, 10th Europe - Asia Congress on Mechatronics
Abbreviated titleMECH2018
CountryJapan
CityTsu, Mie
Period10/09/1812/09/18
Internet address

Keywords

  • task planning
  • sampling-based path planning
  • cost space planning
  • autonomy
  • robotics

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