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Area of Science:

  • Robotics
  • Humanoid Robot Control
  • Vehicle Manipulation

Background:

  • Humanoid robot maneuverability in vehicles is crucial for tasks like obstacle avoidance.
  • Agile steering wheel manipulation is essential for increasing driving speed in confined spaces.
  • Existing human driving strategies include 'Hand-to-Hand,' 'Hand-over-Hand,' and 'One-Hand.'

Purpose of the Study:

  • To quantitatively analyze human driving strategies based on motion data.
  • To propose a novel, efficient manipulation strategy for humanoid robot steering.
  • To develop a control framework for precise steering and force management.

Main Methods:

  • Quantitative analysis of human driving motion data (joint combination range, shoulder motion, manipulation velocity).
  • Development of a friction-driven, one-hand manipulation strategy for humanoid robots.
  • Construction of an operating force model for steering wheel interaction.
  • Implementation of a quadratic programming (QP)-based control framework.

Main Results:

  • Analysis revealed key differences in human driving strategies.
  • The proposed one-hand strategy effectively balances small motion range and rapid manipulation.
  • The operating force model accurately predicts steering wheel resistance.
  • The QP-based controller successfully tracked end-effector position and target wrench output.
  • Maximum rotation velocity achieved was 3.14 rad/s in an obstacle avoidance scenario.

Conclusions:

  • The developed friction-driven, one-hand strategy significantly enhances humanoid robot steering capabilities.
  • The operating force model and QP control framework enable safe and precise steering.
  • This research advances humanoid robot autonomy in complex vehicle driving tasks.