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Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network.

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Summary

This research introduces a robust framework for Unmanned Aerial Vehicle (UAV) swarms in Mobile Edge Computing (MEC). It enhances coordination and overcomes leader failure issues in complex environments.

Keywords:
Mobile Edge ComputingUAVartificial intelligenceformation controlleader electionmulti-agent systemsobstacle avoidancepath planning

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Area of Science:

  • Robotics and Control Systems
  • Mobile Edge Computing (MEC)
  • Internet of Things (IoT)

Background:

  • Unmanned Aerial Vehicle (UAV) swarms offer innovative solutions for Mobile Edge Computing (MEC) and Internet of Things (IoT) applications.
  • Existing centralized leader-follower formations in UAV swarms are vulnerable to mission failure upon leader incapacitation.
  • Coordinating UAV swarms in complex, unknown environments presents significant challenges for reliable operation.

Purpose of the Study:

  • To propose a novel framework for UAV swarms operating within diverse MEC architectures.
  • To address and overcome the limitations of centralized control systems in UAV swarm applications.
  • To enhance the overall performance, safety, and adaptability of UAV swarms.

Main Methods:

  • Development of a framework integrating distributed formation control, online leader election, and collaborative obstacle avoidance.
  • Implementation of an optimal path generation algorithm accounting for obstacles and inter-agent collisions.
  • Design of a quaternion-based sliding mode controller for precise formation control and trajectory tracking.

Main Results:

  • The proposed framework enables UAVs to adapt to various MEC architectures and operational constraints.
  • Successful online leader election mechanism ensures mission continuity despite leader failure.
  • Demonstrated effectiveness in complex scenarios through simulations, validating the framework's capabilities.

Conclusions:

  • The developed framework significantly improves the reliability and performance of UAV swarms in MEC environments.
  • The combination of distributed control, dynamic leader election, and obstacle avoidance enhances operational safety and durability.
  • This research provides a resilient solution for UAV swarm coordination, overcoming critical drawbacks of previous architectures.