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Summary
This summary is machine-generated.

This study introduces a hierarchical fuzzy logic system for precise multi-agent coordination, enabling simultaneous arrival in urban environments. The system ensures collision avoidance and synchronized timing for mobile robots using advanced control strategies.

Keywords:
arrival-time controlfuzzy logic controlhierarchical fuzzy systemmulti-agent controlnavigation

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Multi-Agent Systems

Background:

  • Coordinating multiple mobile agents for simultaneous arrival in complex environments presents significant challenges.
  • Existing methods often struggle with real-time obstacle avoidance and precise temporal synchronization.
  • Urban environments with dynamic obstacles require robust navigation and coordination strategies.

Purpose of the Study:

  • To develop a hierarchical fuzzy logic system for precise coordination of multiple mobile agents.
  • To achieve simultaneous arrival at destination positions while ensuring collision avoidance in cluttered urban settings.
  • To compare two distinct control approaches within the hierarchical framework.

Main Methods:

  • A hierarchical control system with two levels: lower-level individual navigation for obstacle avoidance and higher-level coordination for synchronized arrival.
  • Two proposed approaches: cascading fuzzy logic controllers and a hybrid system combining fuzzy logic with Long Short-Term Memory (LSTM) recurrent neural networks.
  • Optimization of controller parameters using Particle Swarm Optimization (PSO) and enhanced scalability via an interpolation method.

Main Results:

  • The developed hierarchical fuzzy logic system effectively controls agent speeds and directions for simultaneous target achievement.
  • Both proposed approaches demonstrated capability in managing synchronized arrival and obstacle avoidance.
  • The use of a physics-based simulator (Webots) facilitated training and testing of the control models.

Conclusions:

  • The hierarchical fuzzy logic system provides a robust framework for precise multi-agent coordination in challenging environments.
  • The integration of fuzzy logic with advanced techniques like LSTM offers promising avenues for complex robotic tasks.
  • Further research will focus on hardware deployment and validation of the simulated control strategies.