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

TL;DR

AeroResQ is an edge-accelerated UAV framework that enables scalable, resilient, and collaborative wildfire escape route planning using drone fleets with onboard AI and dynamic path computation.

Contribution

The paper introduces AeroResQ, a novel multi-layer UAV framework with collaborative path planning, load balancing, and resilience mechanisms for wildfire scenarios.

Findings

Achieves <=500ms end-to-end latency for route planning

Maintains over 98% success rate in task reassignment and completion

Demonstrates effectiveness in realistic wildfire emulation environments

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.