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Related Experiment Videos

A neural network approach to dynamic task assignment of multirobots.

Anmin Zhu1, Simon X Yang

  • 1Advanced Robotics and Intelligent Systems (ARIS) Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.

IEEE Transactions on Neural Networks
|September 28, 2006
PubMed
Summary

This study introduces a neural network approach using self-organizing maps (SOM) for dynamic task assignment in multirobot systems. The method ensures robots reach target locations efficiently, even with system uncertainties and environmental changes.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Multirobot systems require efficient task assignment in dynamic environments.
  • Uncertainties and environmental changes pose significant challenges to robot coordination.

Purpose of the Study:

  • To propose a novel neural network approach for dynamic task assignment in multirobot systems.
  • To integrate robot motion planning with task assignment for seamless operation.
  • To ensure robust robot navigation and task completion under uncertain conditions.

Main Methods:

  • Utilizing a self-organizing map (SOM) based neural network for task allocation.
  • Integrating robot motion planning directly with the task assignment process.
  • Implementing dynamic adjustments in robot navigation to handle uncertainties.

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Main Results:

  • The proposed SOM-based approach effectively assigns tasks to mobile robots in dynamic environments.
  • Robot navigation is dynamically adjusted to meet target location requirements, even with robot failures.
  • The system demonstrates adaptability to changing environmental conditions.

Conclusions:

  • The SOM-based neural network approach offers an effective and efficient solution for dynamic task assignment in multirobot systems.
  • Integrated motion planning and dynamic navigation enhance system robustness and adaptability.
  • Simulation studies validate the proposed approach's performance in uncertain and changing environments.