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Human-like task planning for a dual-arm robot based on motion primitives

  • Hongcheng Xu*
  • , Xilun Ding
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Task planning has an important effect on the anthropomorphic characteristics of the motion of the humanoid robot. In this paper, human-like task planning for a dual-arm robot is discussed. A bottom-up task planning approach is proposed based on the motion primitives which imply the features of human motion, in order to obtain a human-like motion fundamentally. Unlike the traditional methods which decompose the task into a sequence of well-designed trajectories of the end-effector in task space, we ignore the trajectory planning temporarily, and assemble the motion primitives until the task is accomplished. Therefore, the human-like posture is guaranteed all the time, and there is no need to spend much time in solving the inverse kinematics of redundant manipulators and performing an optimization for a human-like posture. Two basic and specific tasks are analyzed based on the proposed approach. Finally, simulations are performed to verify the validation and feasibility, and the characteristics of the planned motion are discussed.

Original languageEnglish
Title of host publicationProceedings of the 2015 Chinese Intelligent Automation Conference - Intelligent Technology and Systems
EditorsZhidong Deng, Hongbo Li
PublisherSpringer Verlag
Pages411-421
Number of pages11
ISBN (Print)9783662464656
DOIs
StatePublished - 2015
EventChinese Intelligent Automation Conference, 2015 - Fuzhou, China
Duration: 1 Jan 2015 → …

Publication series

NameLecture Notes in Electrical Engineering
Volume338
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceChinese Intelligent Automation Conference, 2015
Country/TerritoryChina
CityFuzhou
Period1/01/15 → …

Keywords

  • Bottom-up approach
  • Dual-arm robot
  • Motion primitive
  • Task planning

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