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Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem
Xiulan Shu1, Anping Lin2, Xupeng Wen3
1School of Intelligent Manufacturing Engineering, Zhanjiang University of Science and Technology, Zhanjiang 524000, China.
Sensors (Basel, Switzerland)
|October 26, 2024
Summary
A new multi-agent deep reinforcement learning algorithm, EMADRL, optimizes drone routes for energy efficiency. This approach significantly reduces energy consumption in last-mile drone distribution, outperforming traditional methods.
Area of Science:
- Robotics and Automation
- Operations Research
- Artificial Intelligence
Background:
- Drone technology is advancing rapidly, increasing the need for efficient distribution strategies.
- Energy consumption is a critical factor in assessing the efficiency of drone distribution.
- Traditional routing algorithms have limitations in finding optimal, energy-efficient drone routes.
Purpose of the Study:
- To address the limitations of traditional routing algorithms in drone distribution.
- To propose a novel algorithm for optimizing energy consumption in drone routing.
- To enhance the energy efficiency of last-mile drone distribution.
Main Methods:
- Formulated the drone routing problem within a multi-agent reinforcement learning framework.
- Developed the Energy-aware Multi-Agent Deep Reinforcement Learning (EMADRL) algorithm.
- Integrated a drone energy consumption model and employed strategy gradient algorithms with attention mechanisms.
Main Results:
- EMADRL consistently achieves high-quality solutions rapidly.
- Demonstrated superior energy efficiency compared to contemporary algorithms.
- Achieved average energy savings of 5.96% and maximum savings of 12.45%.
Conclusions:
- EMADRL offers a promising solution for optimizing energy consumption in drone distribution.
- The algorithm effectively addresses complexities in multi-depot vehicle routing problems.
- This approach advances energy-efficient strategies for last-mile logistics.
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