arXiv cs.MA· Aneesh Calyam, Subrahmanya Chandra Bhamidipati, Zack Murry, Sharan Srinivas·· 11 小时前AI 评分31
CEDA：面向灾后医疗物资配送的跨层多无人机协同算法
A Cross-Layered Multi-Drone Coordination for Medical Supply Delivery during Disaster Response Management
AI 导读
CEDA 是一种面向灾后医疗物资配送的 CTDE 双深度 Q 网络算法，结合 QMIX 式单调混合网络，联合优化分诊感知路由、多智能体协同与节能导航。在五架无人机服务最多 50 名患者的仿真中，CEDA 配送超过 95% 的生成患者，训练中障碍碰撞减少逾 95%，加权分诊效率超过 0.90，Jain 公平性指数约为 1.0。
正文 · 原文
Abstract:Autonomous drone fleets have potential in medical supply delivery during disaster incident response. However, coordinating multiple drones in such settings introduces compounding challenges: dynamic hazards such as wind, obstacles, and intermittent connectivity, constrained energy budgets, and the need to serve patients fairly under strict deadlines and triage-based priority. We present CEDA, a Centralized Training with Decentralized Execution (CTDE) Double Deep Q-Network algorithm augmented with a QMIX-style monotonic mixing network for cooperative multi-drone medical delivery, designed to jointly optimize triage-aware routing, multi-agent coordination, and energy-efficient navigation under dynamic uncertainty. CEDA introduces a Priority-Preserving Fair Scheduling strategy, in which a structured reward function encodes triage priority weights, outcome-level fairness and priority-ordering rewards, and complementary shaping mechanisms that discourage systematic starvation. We evaluate CEDA in a simulated grid environment with five drones serving up to fifty patients, featuring dynamic hazard zones, stochastic action failures, and dynamically spawning patients across three triage levels, and in PX4 software-in-the-loop (SITL) validation using five X500 quadrotors controlled via MAVSDK. Simulation results show that CEDA delivers over 95\% of spawned patients, reduces obstacle collisions by over 95\% across training, and achieves a weighted triage efficiency exceeding 0.90 alongside a Jain's Fairness Index of approximately 1.0 across triage classes. CEDA preserves clinical priority ordering, with Critical patients receiving the fastest response times, while achieving low and comparable mortality across all triage classes. PX4 SITL validation further shows that the learned policy retains its triage-aware behavior under continuous simulated flight and delayed-action conditions.
| Comments: | 18 pages, 7 figures, 7 tables |
| Subjects: | Multiagent Systems (cs.MA); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.09342 [cs.MA] |
| (or arXiv:2605.09342v2 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09342 arXiv-issued DOI via DataCite |
Submission history
From: Subrahmanya Chandra Bhamidipati [view email]
[v1]
Sun, 10 May 2026 05:43:51 UTC (5,043 KB)
[v2]
Fri, 30 Oct 2026 17:15:32 UTC (3,414 KB)
来源：arXiv cs.MA · arxiv.org