AeroResQ: Edge-Accelerated UAV Framework for Scalable, Resilient and Collaborative Escape Route Planning in Wildfire Scenarios

2025-10-27

AI Summary

To address scalability and resilience limitations in drone swarm collaborative evacuation path planning for wildfire emergencies, this paper proposes an edge-accelerated multi-agent coordination framework. Methodologically, it introduces a hierarchical coordination architecture and a context-aware weighted A* path planning algorithm, integrating onboard edge computing, lightweight deep neural network (DNN) models for fire detection and human pose estimation, Apache IoTDB for time-series data storage, and a dynamic load-balancing mechanism. The framework enables seamless data migration and intra-geofence task reallocation upon drone failure. Experimental evaluation in a Southern California wildfire simulation environment demonstrates end-to-end latency ≤500 ms and task reallocation success rate >98%, significantly enhancing real-time responsiveness and system fault tolerance.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Drone fleets equipped with onboard cameras, computer vision, and Deep Neural Network (DNN) models present a powerful paradigm for real-time spatio-temporal decision-making. In wildfire response, such drones play a pivotal role in monitoring fire dynamics, supporting firefighter coordination, and facilitating safe evacuation. In this paper, we introduce AeroResQ, an edge-accelerated UAV framework designed for scalable, resilient, and collaborative escape route planning during wildfire scenarios. AeroResQ adopts a multi-layer orchestration architecture comprising service drones (SDs) and coordinator drones (CDs), each performing specialized roles. SDs survey fire-affected areas, detect stranded individuals using onboard edge accelerators running fire detection and human pose identification DNN models, and issue requests for assistance. CDs, equipped with lightweight data stores such as Apache IoTDB, dynamically generate optimal ground escape routes and monitor firefighter movements along these routes. The framework proposes a collaborative path-planning approach based on a weighted A* search algorithm, where CDs compute context-aware escape paths. AeroResQ further incorporates intelligent load-balancing and resilience mechanisms: CD failures trigger automated data redistribution across IoTDB replicas, while SD failures initiate geo-fenced re-partitioning and reassignment of spatial workloads to operational SDs. We evaluate AeroResQ using realistic wildfire emulated setup modeled on recent Southern California wildfires. Experimental results demonstrate that AeroResQ achieves a nominal end-to-end latency of <=500ms, much below the 2s request interval, while maintaining over 98% successful task reassignment and completion, underscoring its feasibility for real-time, on-field deployment in emergency response and firefighter safety operations.

Problem

• Planning scalable escape routes for wildfire evacuations using drones
• Detecting stranded individuals with edge-accelerated computer vision models
• Ensuring resilient drone coordination through automated failure recovery mechanisms

Innovation

• Edge-accelerated UAV framework for wildfire escape planning
• Multi-layer drone orchestration with specialized service roles
• Weighted A* algorithm with resilient load-balancing mechanisms