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Self-Triggered Formation Control of Nonholonomic Robots.

Carlos Santos1, Felipe Espinosa2, Miguel Martinez-Rey3

  • 1Electronics Department, University of Alcalá, Engineering School, Campus Universitario, 28871 Alcalá de Henares, Spain. carlos.santos@uah.es.

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Summary
This summary is machine-generated.

This study introduces a self-triggered aperiodic remote formation controller for nonholonomic robots. The controller reduces wireless communication by updating signals based on Lyapunov function variations, ensuring stable trajectory tracking.

Keywords:
formation controlnonlinear trajectory trackingpractical stabilityreal-time schedulingremote guidanceself-triggered Lyapunov control

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Area of Science:

  • Robotics
  • Control Systems
  • Wireless Communication

Background:

  • Nonholonomic robots require robust control for trajectory tracking.
  • Minimizing wireless communication is crucial for efficient robotic systems.
  • Aperiodic control offers potential for reduced communication overhead compared to periodic methods.

Purpose of the Study:

  • To design an aperiodic remote formation controller for nonholonomic robots.
  • To reduce wireless communication between robots and external sensors.
  • To guarantee formation stability during trajectory tracking.

Main Methods:

  • Developed a self-triggered controller updating signals based on Lyapunov function variation.
  • Implemented centralized and decentralized triggering mechanisms.
  • Utilized an external positioning sensor network for robot pose detection.

Main Results:

  • The proposed controller significantly reduces communication needs compared to periodic implementations.
  • Desired tracking performance is preserved with reduced communication.
  • Statistical analysis of simulations and experimental validation confirm effectiveness.

Conclusions:

  • The aperiodic self-triggered controller effectively reduces communication in robotic formations.
  • Lyapunov function-based triggering ensures stable trajectory tracking.
  • The approach is validated through simulations and real-world robotic experiments.