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Particle Swarm Optimization aided PID gait controller design for a humanoid robot.

Abhishek Kumar Kashyap1, Dayal R Parhi1

  • 1Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela 769008, Odisha, India.

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|December 28, 2020
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Summary

This study introduces a Particle Swarm Optimization (PSO) tuned Proportional-Integral-Derivative (PID) controller for humanoid robot NAO gait planning. The optimized controller enhances obstacle avoidance, reduces travel time, and improves robot stabilization.

Keywords:
Gait planningHumanoid robot NAOLinear inverted pendulum modelParticle swarm optimizationProportional–integral–derivative controllerZero moment point

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Humanoid robot gait planning is crucial for task execution.
  • Balancing on two feet requires specialized gait analysis.
  • The Linear Inverted Pendulum Model (LIPM) simplifies gait studies.

Purpose of the Study:

  • To develop an optimized gait planning strategy for the NAO humanoid robot.
  • To improve obstacle avoidance and robot stabilization during navigation.
  • To reduce computational complexity and enhance gait predictability.

Main Methods:

  • Utilized the Linear Inverted Pendulum Model (LIPM) with Center of Mass (COM) and Zero Moment Point (ZMP) criteria.
  • Implemented a Proportional-Integral-Derivative (PID) controller integrated with sensory data and inverse kinematics.
  • Employed Particle Swarm Optimization (PSO) to tune PID controller parameters for optimal navigation and obstacle avoidance.

Main Results:

  • The PSO-tuned PID controller demonstrated improved obstacle avoidance and robot stabilization.
  • Achieved a 25% reduction in overshoot and decreased stabilization time.
  • Simulations and experiments on a real NAO robot showed results with less than 6% deviation.

Conclusions:

  • The proposed PSO-tuned PID controller offers a robust and efficient solution for humanoid robot gait planning.
  • It enhances navigation capabilities, reduces travel time, and ensures stable robot movement.
  • The controller's performance was validated through comparative studies and statistical analysis.