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Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey.

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Summary
This summary is machine-generated.

This survey reviews bio-inspired algorithms for unmanned aerial vehicle (UAV) path planning. It highlights their advantages in addressing complex challenges in UAV communications, offering a comprehensive overview for researchers.

Keywords:
UAV communicationUAV path planningbio-inspired algorithmoptimization algorithmpath planningunmanned aerial vehicle

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Area of Science:

  • Computer Science
  • Robotics
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) offer flexible network deployment but face challenges in throughput, delay, cost, and energy.
  • Path planning is critical for efficient UAV communication networks.

Purpose of the Study:

  • To investigate and survey bio-inspired algorithms for UAV path planning over the last decade.
  • To provide a comprehensive review of existing bio-inspired algorithms for UAV path planning, a topic not yet extensively covered.

Main Methods:

  • Literature review focusing on bio-inspired optimization algorithms applied to UAV path planning.
  • Analysis of algorithms based on key features, working principles, advantages, and limitations.
  • Comparative study of path planning algorithms based on performance factors.

Main Results:

  • Identified and analyzed prevailing bio-inspired algorithms for UAV path planning.
  • Compared various algorithms based on their characteristics and performance.
  • Summarized current challenges and future research directions in the field.

Conclusions:

  • Bio-inspired algorithms show significant potential for solving complex UAV path planning problems with nonlinear constraints.
  • This survey provides a foundational resource for understanding and advancing bio-inspired UAV path planning techniques.
  • Future research should focus on addressing identified challenges and exploring emerging trends in UAV path planning.