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

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.