The paper presents a novel approach, Distributed Optimization and (deep) Reinforcement Learning (DO-RL), to coordinate a swarm of autonomous drones for efficient traffic monitoring in smart cities.
The key highlights are:
The paper tackles the problem of coordinating drones for improved distributed navigation, sensing, and recharging. The goal is to enhance sensing quality in a large-scale area using both drones and charging stations while minimizing the overall involved costs.
DO-RL integrates both distributed optimization and deep reinforcement learning (DRL) to address this problem. Drones independently determine their flying direction and recharging place using DRL, while adapting navigation and sensing through distributed optimization to improve energy-efficiency.
A structured tree communication model is designed to enable efficient information exchange among drones while retaining their decision-making autonomy. This ensures scalability, efficiency, decentralization, and resilience.
Extensive experimentation with realistic urban mobility datasets demonstrates the outstanding performance of DO-RL compared to state-of-the-art methods. The results show that long-term DRL methods optimize scarce drone resources for traffic management, while the integration of short-term distributed optimization is crucial for advising on charging policies and maintaining battery safety.
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by Chuhao Qin,E... at arxiv.org 04-03-2024
https://arxiv.org/pdf/2311.09852.pdfDeeper Inquiries