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Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution.

Jesus Hernandez-Barragan1, Carlos Lopez-Franco1, Nancy Arana-Daniel1

  • 1Department of Computer Science, University of Guadalajara, Guadalajara, Jalisco, Mexico.

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Summary
This summary is machine-generated.

This study introduces a novel method for solving the inverse kinematics of cooperative mobile manipulators, achieving precise path tracking without Jacobian matrix inversion. The self-adaptive differential evolution algorithm offers an effective solution for complex manipulation tasks.

Keywords:
Cooperative systemsDifferential evolutionInverse kinematicsMobile manipulators

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Area of Science:

  • Robotics
  • Control Systems
  • Optimization Algorithms

Background:

  • Cooperative mobile manipulators are crucial for complex manipulation tasks.
  • Solving inverse kinematics for these systems is challenging due to singularities and joint constraints.
  • Existing methods often rely on Jacobian inversion, which can fail in singular configurations.

Purpose of the Study:

  • To present a robust approach for solving the inverse kinematics of cooperative mobile manipulators.
  • To address challenges related to singularities and joint limits in coordinate manipulation tasks.
  • To validate the proposed method through simulations and experimental setups.

Main Methods:

  • A self-adaptive differential evolution algorithm is employed to solve the inverse kinematics as a global constrained optimization problem.
  • A kinematics model for a system of two omnidirectional platform manipulators with n degrees of freedom (DOF) is developed.
  • An objective function is formulated using forward kinematics equations, incorporating penalty functions for joint limit constraints.

Main Results:

  • The proposed approach effectively solves the inverse kinematics without requiring Jacobian matrix inversion, thus avoiding singularities.
  • Simulation experiments demonstrate precise and accurate results for coordinate path tracking tasks.
  • Experimental validation using KUKA Youbot systems confirms the practical applicability of the method.

Conclusions:

  • The self-adaptive differential evolution algorithm provides an effective and singularity-free solution for the inverse kinematics of cooperative mobile manipulators.
  • The developed objective function successfully handles joint limit constraints.
  • The approach is demonstrated to be accurate and applicable for real-world robotic manipulation tasks.