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

TL;DR

AeroResQ presents an edge-accelerated UAV framework enabling scalable, resilient, and collaborative escape route planning during wildfires by deploying service drones for ground surveillance and coordinator drones for route planning, all backed by a lightweight onboard datastore (IoTDB) with data replication. The system uses a weighted A* search over a fire-pruned, elevation-aware ground graph to generate safe, efficient evacuation routes, while a heartbeat-based resilience layer rebalances workloads and reassigns tasks in case of drone failures. Empirical evaluation on realistic Southern California wildfire data demonstrates end-to-end latency of $\leq 500$ ms per evacuation request and high task reassignment success, validating real-time feasibility for emergency response. The work advances wildfire response by integrating edge AI, collaborative planning, and fault-tolerant data management, with Docker-Compose emulation enabling scalable testing and future extensions toward broader network resilience and larger fleets.

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.

Figures (11)

• Figure 1: Problem Overview.
• Figure 2: Sample images from drones: (a) Palisades fire (b) FlameVisionJafar2023FlameVision
• Figure 3: Workflow and Execution Sequence for AeroResQ.
• Figure 4: Effect of elevation gain penalty on route generation by A* causes route to change.
• Figure 5: Safe locations around fire polygons.