A Heterogeneous Dual-Network Framework for Emergency Delivery UAVs: Communication Assurance and Path Planning Coordination
career value
202K/year
🤖 AI Summary
This study addresses the instability of command-and-control links for emergency delivery drones caused by ground infrastructure damage following disasters, which can lead to loss of control and delayed relief operations. To mitigate this, the paper proposes a Heterogeneous Dual-Network Framework (HDNF) that jointly deploys an Emergency Communication Support Network (ECSN) and a Delivery Path Network (DPN). By co-optimizing task assignment, three-dimensional base station placement, and path planning, HDNF ensures end-to-end control reliability while balancing energy consumption and deployment cost. The framework innovatively integrates a dynamic communication assurance mechanism for mission-critical 3D flight corridors, tightly coupling communication coverage with delivery trajectories. A hierarchical solution strategy is devised to tackle the high-dimensional NP-hard problem, combining a multi-layer command-and-control model, 3D coverage-aware multi-agent reinforcement learning, and a communication-aware A* path planner. Simulations demonstrate that HDNF significantly enhances link reliability, eliminates critical-phase communication outages, maintains high mission success rates, and reduces deployment costs.
Application Category
📝 Abstract
Natural disasters often damage ground infrastructure, making unmanned aerial vehicles (UAVs) essential for emergency supply delivery. Yet safe operation in complex post-disaster environments requires reliable command-and-control (C2) links; link instability can cause loss of control, delay rescue, and trigger severe secondary harm. To provide continuous three-dimensional (3D) C2 coverage during dynamic missions, we propose a Heterogeneous Dual-Network Framework (HDNF) for safe and reliable emergency delivery. HDNF tightly couples an Emergency Communication Support Network (ECSN), formed by hovering UAV base stations, with a Delivery Path Network (DPN), formed by fast-moving delivery UAVs. The ECSN dynamically safeguards mission-critical flight corridors, while the DPN aligns trajectories with reliable coverage regions. We formulate a joint optimization problem over task assignment, 3D UAV-BS deployment, and DPN path planning to maximize end-to-end C2 reliability while minimizing UAV flight energy consumption and base-station deployment cost. To solve this computationally intractable NP-hard problem, we develop a layered strategy with three components: (i) a multi-layer C2 service model that overcomes 2D-metric limitations and aligns UAV-BS deployment with mission-critical 3D phases; (ii) a 3D coverage-aware multi-agent reinforcement learning algorithm that addresses the high-dimensional search space and improves both training efficiency and topology resilience; and (iii) a 3D communication-aware A* planner that jointly optimizes C2 quality and flight energy, mitigating trajectory--coverage mismatch and improving routing safety. Extensive simulations show that HDNF markedly improves C2 reliability, eliminates outages in critical phases, and sustains high task success rates while reducing hardware deployment cost.
Problem
Research questions and friction points this paper is trying to address.
Emergency Delivery UAVs
Command-and-Control (C2) Communication
3D Coverage
Path Planning
Communication Assurance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heterogeneous Dual-Network Framework
3D Communication Coverage
Multi-agent Reinforcement Learning
Communication-aware Path Planning
Emergency UAV Delivery
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