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Efficient path planning for UAV formation via comprehensively improved particle swarm optimization.

Shikai Shao1, Yu Peng1, Chenglong He2

  • 1School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.

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|August 17, 2019
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Summary
This summary is machine-generated.

This study introduces an improved particle swarm optimization (PSO) for autonomous unmanned aerial vehicle (UAV) formation path planning. The enhanced PSO algorithm significantly boosts planning speed and path optimality for UAVs.

Keywords:
Mutation strategyParticle swarm optimization (PSO)Path planningUAV formation

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Autonomous unmanned aerial vehicle (UAV) formation systems require efficient path planning.
  • Current path planning methods face challenges in rapidity and optimality.

Purpose of the Study:

  • To develop a novel 3D path planning algorithm for UAV formations.
  • To enhance the speed and optimality of automatic path planning using an improved Particle Swarm Optimization (PSO).

Main Methods:

  • A comprehensively improved PSO algorithm is proposed for 3D path planning.
  • Chaos-based Logistic map for improved initial particle distribution.
  • Adaptive linear-varying acceleration coefficients and maximum velocity.
  • A mutation strategy to replace undesired particles with desired ones.

Main Results:

  • The improved PSO algorithm accelerates convergence speed.
  • Enhanced solution optimality is achieved for UAV path planning.
  • Monte-Carlo simulations demonstrate the method's rapidity and optimality under terrain and threat constraints.

Conclusions:

  • The proposed comprehensively improved PSO is effective for autonomous UAV formation path planning.
  • The algorithm offers significant improvements in both speed and optimality.
  • Validated through simulations, the method addresses key challenges in UAV formation control.