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Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot.

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  • 1Daegu Research Center for Medical Devices and Rehabilitation, Korea Institute of Machinery and Materials, Daegu 42994, Korea.

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Summary

This study introduces a novel control structure for omni-wheel mobile robots (OWMRs), utilizing bio-inspired brain limbic system (BLS)-based control for enhanced performance. The proposed system demonstrates superior motion control and optimal path planning, outperforming existing methods.

Keywords:
A*brain limbic systemfuzzy analytic hierarchy processomni-wheel mobile robot

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Omni-wheel mobile robots (OWMRs) require robust and fast control for complex environments.
  • Existing control methods like PID may not meet the performance demands for OWMRs.
  • Bio-inspired control strategies offer potential for improved robotic system performance.

Purpose of the Study:

  • To develop an advanced control structure for OWMRs.
  • To enhance path planning and motion control capabilities.
  • To validate the proposed control structure's effectiveness through simulations.

Main Methods:

  • Derivation of the OWMR kinematic model.
  • Design of a brain limbic system (BLS)-based motion controller.
  • Integration of A* algorithm and fuzzy analytic hierarchy process (FAHP) for optimal path planning.
  • Numerical simulations for performance evaluation.

Main Results:

  • The BLS-based motion controller demonstrated superior performance compared to PID controllers in point-to-point, circular path, and random target tracking tasks.
  • The A*-FAHP path planning algorithm successfully generated optimal, collision-free paths in static and dynamic warehouse environments, and autonomous parking scenarios.
  • The proposed control structure achieved robustness and fast control performance.

Conclusions:

  • The proposed control structure, integrating BLS-based control and A*-FAHP path planning, significantly enhances OWMR performance.
  • The developed system offers a robust and efficient solution for complex robotic navigation tasks.
  • This research provides a foundation for advanced autonomous robotic systems.