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Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

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  • 1Key Laboratory of Network and Communications, Dalian University, Dalian 116622, China.

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Summary

This study introduces an improved Path Optimization Algorithm (POA) for Unmanned Aerial Vehicle (UAV) path planning. The enhanced algorithm significantly improves path efficiency and safety in urban environments.

Keywords:
UAVintelligent optimization algorithmmulti-objective optimizationpath planning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Effective Unmanned Aerial Vehicle (UAV) path planning is crucial for optimizing tasks in complex urban environments.
  • Existing algorithms often struggle with multi-objective optimization, balancing path length, turning angles, and collision avoidance.

Purpose of the Study:

  • To develop an advanced UAV path planning algorithm that enhances efficiency and safety.
  • To address the multi-constraint optimization challenge in UAV navigation.

Main Methods:

  • A multi-strategy improved Path Optimization Algorithm (IPOA) was developed.
  • IPOA integrates chaotic mapping, refracted reverse learning, nonlinear inertia weights, Levy flight, and adaptive t-distribution variation.
  • The algorithm transforms path planning into a multi-constraint optimization problem considering path length, turning angle, and collision avoidance.

Main Results:

  • IPOA demonstrated superior performance against other algorithms in 69.4% of CEC2022 test functions.
  • In real-world simulations, IPOA improved path length by 8.44%, turning angle by 5.82%, obstacle avoidance by 4.07%, and flight time by 9.36% compared to the original POA.
  • Compared to MPOA, IPOA showed improvements of 4.09% in path length, 0.76% in turning angle, 1.85% in obstacle avoidance, and 4.21% in flight time.

Conclusions:

  • The proposed IPOA algorithm significantly enhances UAV path planning efficiency and accuracy.
  • IPOA offers a robust solution for complex urban navigation tasks, outperforming existing methods.