无人机群实时规划逃生路线，500毫秒内完成响应，98%任务可恢复
AeroResQ: Edge-Accelerated UAV Framework for Scalable, Resilient and Collaborative Escape Route Planning in Wildfire Scenarios
- 分层架构：巡检机发现人员，指挥机用加权A*算法生成最优路径
- 端侧加速：边缘计算实现火情与人体姿态检测，端到端延迟低于500毫秒
- 高鲁棒性：设备故障自动重分配任务，适合野外应急救援场景
搭载摄像头、计算机视觉和深度神经网络（DNN）模型的无人机群，为实时时空决策提供了强大范式。在野火救援中，此类无人机在监测火势动态、支援消防员协同和保障安全撤离方面发挥关键作用。本文提出AeroResQ，一种面向野火场景下可扩展、高韧性、协同式的逃逸路径规划边缘加速无人机框架。AeroResQ采用多层编排架构，包含执行巡检任务的服务无人机（SDs）和负责协调的指挥无人机（CDs）。SDs通过本地边缘加速器运行火情检测与人体姿态识别DNN模型，探测受困人员并发起援助请求；CDs配备轻量级数据存储（如Apache IoTDB），动态生成最优地面逃生路径，并实时监控消防员行进状态。该框架提出基于加权A*搜索的协同路径规划方法，由CDs计算上下文感知的逃生路径。AeroResQ进一步集成智能负载均衡与容错机制：当CD故障时，自动在IoTDB副本间重新分配数据；当SD故障时，触发地理围栏式工作负载再分区与重分配至可用的SDs。我们在模拟近期南加州野火场景的真实环境中评估AeroResQ。实验结果表明，其端到端延迟不超过500毫秒，远低于2秒的请求间隔，且任务成功重分配与完成率超过98%，验证了其在应急响应与消防员安全保障中的实时部署可行性。
原文摘要 · Abstract (English)
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.
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