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Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm.

Changchun Zhang^1,2, Yifan Liu^1,2, Chunhe Hu^1,2

¹School of Technology, Beijing Forestry University, Beijing 100083, China.

Biomimetics (Basel, Switzerland)

|December 22, 2022

Summary

This summary is machine-generated.

The Gray Wolf (GWO) algorithm optimizes multi-unmanned aerial vehicle (UAV) path planning for simultaneous arrival, enhancing safety and efficiency. This GWO method shows superior performance in minimizing flight time errors compared to other algorithms.

Keywords:

GWO multi-UAV path planning swarm intelligence time windows

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Area of Science:

Robotics
Artificial Intelligence
Aerospace Engineering

Background:

Multi-unmanned aerial vehicle (UAV) systems require sophisticated path planning for complex missions.
Existing path planning methods often overlook synchronized arrival constraints and time windows.
Ensuring collision avoidance and adherence to UAV operational constraints is critical.

Purpose of the Study:

To develop and evaluate an optimized path planning strategy for multiple UAVs using the Gray Wolf (GWO) algorithm.
To incorporate time window constraints for simultaneous UAV arrival at destination points.
To enhance the safety and efficiency of multi-UAV cooperative flight operations.

Main Methods:

The Gray Wolf (GWO) algorithm was adapted for multi-UAV path planning, integrating time window considerations.
Path planning focused on threat avoidance, UAV self-constraints, and inter-UAV obstacle avoidance.
Experimental validation was performed to assess the efficacy of the proposed GWO-based approach.

Main Results:

The GWO algorithm successfully planned safe flight paths for multiple UAVs, enabling simultaneous arrival.
The mean error in flight time synchronization among UAVs using GWO was 0.213.
GWO demonstrated superior performance in time synchronization compared to Particle Swarm Optimization (PSO), Artificial Fish School Optimization (AFO), and Genetic Algorithm (GA).

Conclusions:

The proposed GWO-based method effectively addresses the multi-UAV path planning problem with time window constraints.
Simultaneous arrival and safe path planning were achieved, outperforming existing algorithms in time error reduction.
This research contributes a robust solution for coordinated multi-UAV missions requiring precise temporal synchronization.