Three-Dimensional UAV Path Planning Based on Multi-Strategy Integrated Artificial Protozoa Optimizer.

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Optimization Algorithms

Background:

• Three-dimensional Unmanned Aerial Vehicle (UAV) path planning faces challenges with existing metaheuristic algorithms, including slow convergence and local optima entrapment.
• Efficient and reliable path planning is critical for practical UAV applications.

Purpose of the Study:

• To propose a novel multi-strategy Integrated Artificial Protozoa Optimization (IAPO) algorithm for addressing limitations in 3D UAV path planning.
• To enhance the performance of metaheuristic algorithms in terms of convergence speed, accuracy, and global optimum seeking.

Main Methods:

• Initial population enhancement using tent map and refractive opposition-based learning (ROBL).
• Dynamic optimal leadership mechanism in the autotrophic foraging stage for accelerated convergence and exploration.
• Cauchy mutation strategy during reproduction to avoid local optima.
• Integration of simulated annealing to refine solutions and improve convergence efficiency.

Main Results:

• The IAPO algorithm demonstrated superior performance compared to nine common algorithms on 12 benchmark functions from the 2022 IEEE CEC.
• Achieved faster convergence speed and higher optimization accuracy in benchmark tests.
• Successfully generated collision-free and energy-efficient UAV paths in diverse 3D environments.

Conclusions:

• The proposed IAPO algorithm effectively overcomes the limitations of traditional methods for 3D UAV path planning.
• IAPO offers a robust and efficient solution for generating optimal UAV trajectories.
• The algorithm shows significant potential for practical deployment in complex 3D navigation tasks.