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Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm.

Yuwen Fu1, Shuai Yang2, Bo Liu3

  • 1School of Automation, Central South University, Changsha 410017, China.

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|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces a Modified Cheetah Optimization (MCO) algorithm for autonomous trajectory planning in unmanned aerial vehicles (UAVs). The MCO algorithm enhances cooperative planning for multiple UAVs in complex environments, improving efficiency and performance.

Keywords:
adaptive search agent strategyautonomous trajectory planninglogistic chaotic mapping strategymodified cheetah optimization algorithmmulti-unmanned aerial vehicles

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Autonomous functionality is crucial for unmanned aerial vehicle (UAV) advancement.
  • Intelligent algorithms for autonomous trajectory planning are key to enhancing UAV capabilities.
  • Planning UAV trajectories in complex 3D environments presents significant challenges.

Purpose of the Study:

  • To propose a novel multi-UAV cooperative trajectory-planning method for complex 3D environments.
  • To enhance autonomous behavior and operational efficiency of UAVs through intelligent planning.
  • To address limitations in existing trajectory planning algorithms for cooperative UAV missions.

Main Methods:

  • Developed a spatiotemporal cooperative trajectory planning model with UAV-cooperative and performance constraints.
  • Introduced evaluation criteria including fuel consumption, altitude, and threat distribution cost functions.
  • Proposed a Modified Cheetah Optimization (MCO) algorithm, enhancing the parent Cheetah Optimization (CO) with logistic chaotic mapping, adaptive search agents, and an improved home-returning mechanism.

Main Results:

  • The MCO algorithm demonstrated superior performance compared to other algorithms in simulations.
  • Achieved smaller trajectory costs, faster convergence speed, and stabler performance in trajectory planning.
  • Successfully applied dimensionality reduction searching for real-world autonomous trajectory planning scenarios.

Conclusions:

  • The MCO algorithm is a correct, effective, and advanced solution for multi-UAV cooperative trajectory planning.
  • The proposed method significantly improves UAVs' autonomous functionality in complex environments.
  • This research contributes to the advancement of intelligent algorithms for autonomous systems.