Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow

Area of Science:

• Logistics and Supply Chain Management
• Robotics and Automation
• Operations Research

Background:

• Unmanned Aerial Vehicle (UAV) delivery offers cost reduction potential but faces load and energy constraints.
• Existing logistics models struggle with efficient wide-area distribution for densely located depots.

Purpose of the Study:

• To propose a cooperative truck-UAV delivery scheme to extend UAV operational range and improve delivery success rates.
• To develop novel algorithms for task allocation and path planning in a truck-independent UAV system for efficient logistics.

Main Methods:

• A minimum-cost, maximum-flow model was formulated to determine optimal sub-paths for delivery tasks.
• Resource tree-based algorithms were employed for constructing integrated global paths for both trucks and UAVs.
• Simulations were conducted to evaluate the performance of the proposed cooperative delivery scheme.

Main Results:

• The cooperative strategy significantly extends UAV coverage and enhances overall delivery completion rates.
• The developed algorithms reduced total energy consumption by 11.53% and 9.15% across different task distributions.
• The truck-UAV system demonstrated superior efficiency compared to traditional methods in simulations.

Conclusions:

• The proposed cooperative truck-UAV delivery scheme presents a viable and efficient solution for modern logistics challenges.
• This integrated approach effectively addresses the limitations of standalone UAV delivery systems.
• The findings highlight the potential for substantial improvements in logistics efficiency and energy savings.