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Multi-UAV Path Planning Algorithm Based on BINN-HHO.

Sen Li1,2, Ran Zhang1,2, Yuanming Ding2

  • 1School of Information Engineering, Dalian University, Dalian 116622, China.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for multiple unmanned aerial vehicles (UAVs) to navigate complex 3D mountain environments. The BINN-HHO algorithm enhances path planning and dynamic obstacle avoidance, improving flight stability and efficiency.

Keywords:
Harris hawks optimizationbioinspired neural networkdynamic obstacle avoidanceenergy cycle decline mechanismmultiple unmanned aerial vehicles

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Multiple unmanned aerial vehicles (UAVs) face challenges in 3D mountain environments, including path instability, lengthy planned routes, and inefficient dynamic obstacle avoidance.
  • Existing path planning algorithms struggle to address the complexities of real-time navigation and obstacle management for UAV swarms in challenging terrains.

Purpose of the Study:

  • To propose a novel multi-UAV path planning algorithm, the bio-inspired neural network and improved Harris hawks optimization with periodic energy decline regulation (BINN-HHO), for 3D environments.
  • To enhance the stability, reduce path length, and improve the dynamic obstacle avoidance efficiency of multiple UAVs operating in complex mountainous regions.

Main Methods:

  • Developed a BINN-HHO algorithm integrating a bio-inspired neural network with an enhanced Harris hawks optimization (HHO) incorporating a periodic energy decline regulation mechanism.
  • Implemented an energy cycle decline mechanism within the HHO's energy function to balance global exploration and local search during path planning.
  • Enabled dynamic obstacle avoidance through local path replanning by the improved BINN algorithm when onboard sensors detect obstacles, with automatic return to the global path upon obstacle clearance.

Main Results:

  • The BINN-HHO algorithm demonstrated superior performance in path planning compared to basic Harris hawks optimization, particle swarm optimization (PSO), and sparrow search algorithm (SSA).
  • Significant improvements were observed in dynamic obstacle avoidance efficiency, ensuring safer and more effective UAV operations.
  • The proposed method effectively balances exploration and exploitation, leading to more optimized and stable flight paths.

Conclusions:

  • The BINN-HHO algorithm offers a robust solution for multi-UAV path planning in challenging 3D environments.
  • The integration of periodic energy decline regulation and bio-inspired neural networks significantly enhances navigation capabilities, particularly in dynamic obstacle scenarios.
  • This research contributes to advancing autonomous navigation systems for UAV swarms in complex and unpredictable operational settings.