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Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic-Swarm Optimization for the Inverse

Mohammad Soleimani Amiri1, Rizauddin Ramli1

  • 1Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces a hybrid Genetic-Swarm Optimization (GSO) to precisely control robotic arm movements. This novel approach efficiently solves Inverse Kinematics (IK) problems for accurate target positioning.

Keywords:
Genetic AlgorithmPID controlParticle Swarm Optimizationrobotic arm

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Area of Science:

  • Robotics
  • Control Systems
  • Computational Intelligence

Background:

  • Accurate control of complex multi-joint structures like robotic arms is crucial for diverse applications.
  • Existing methods for Inverse Kinematics (IK) solutions may require optimization for enhanced precision and efficiency.
  • Robotic arm manipulation demands sophisticated control strategies to achieve precise target positioning.

Purpose of the Study:

  • To present a hybrid optimal Genetic-Swarm Optimization (GSO) for solving the Inverse Kinematic (IK) problem in robotic arms.
  • To optimize Proportional-Integral-Derivative (PID) controllers for individual robotic arm joints using GSO.
  • To validate the efficiency of the proposed GSO-based control system in a simulated environment.

Main Methods:

  • A hybrid Genetic-Swarm Optimization (GSO) approach was developed, combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
  • The dynamic model of the robotic arm was determined using the Lagrangian method.
  • PID controller tuning for each joint was treated as an optimization problem solved by PSO within a virtual environment, integrated with a Graphical User Interface (GUI).

Main Results:

  • The hybrid optimal GSO effectively solved the IK problem for each joint of the robotic arm.
  • The GSO-optimized PID controllers demonstrated efficient system performance in achieving accurate target positioning.
  • Statistical analysis confirmed the superior performance of the hybrid optimal GSO compared to conventional optimization methods.

Conclusions:

  • The proposed hybrid optimal GSO provides an efficient and accurate solution for the Inverse Kinematic problem in robotic arms.
  • The integration of GSO with PID control offers a robust framework for complex robotic system control.
  • The developed system, featuring a GUI, shows significant potential for practical applications requiring precise robotic arm manipulation.