Generating human-like movements for robotic arms

Jing Zhao, Biyun Xie, Chunyu Song

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

In this paper, the generation of trajectories of both end-effector and joints for human-like reaching and grasping motions is studied. In reaching movement, the human-like end-effector trajectory is obtained based on the minimum jerk model. A total potential energy criterion is constructed to resolve the kinematic redundancy of human arm in the target position. Gradient Projection Method (GPM) is adopted to trace the human-like end-effector trajectory while minimizing the total potential energy to generate the human-like joint trajectory. In grasping movement, the total potential energy and wrist discomfort are synthesized to resolve the kinematic redundancy of human arm in the target position and orientation. A new planner, Gradient Projection Method based Rapidly-exploring Random Tree (GPM-RRT) algorithm, is proposed to generate the human-like end-effector trajectory by goal biasing and the human-like joint trajectory by minimizing the synthesis of the total potential energy and wrist discomfort. The criteria and algorithm are verified by simulations and experiments.

Original languageEnglish
Pages (from-to)107-128
Number of pages22
JournalMechanism and Machine Theory
Volume81
DOIs
StatePublished - Nov 2014

Bibliographical note

Funding Information:
The support of this research by the National Natural Science Foundation of China and Doctoral Fund of Innovation of Beijing University of Technology (No. YB201303 ) are appreciated.

Keywords

  • Human arm redundancy resolution
  • Human-like movements
  • Kinematics
  • Optimal sampling-based algorithms
  • Robotic arms

ASJC Scopus subject areas

  • Bioengineering
  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications

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