Offloading services to UAV swarms for delay-sensitive tasks in Emergency UAV Networks (EUN) can greatly enhance rescue efficiency. Most task-offloading strategies assumed that UAVs were location-fixed and capable of handling all tasks. However, in complex disaster environments, UAV locations often change dynamically, and the heterogeneity of on-board resources presents a significant challenge in optimizing task scheduling in EUN to minimize latency. To address these problems, a Finite state machines-based Path-following Collaborative computation strategy (FPC) for emergency UAV swarms is proposed. First, an Extended Finite State Machine Space-time Graph (EFSMSG) model is constructed to accurately characterize on-board resources and state transitions while shielding the EUN dynamic characteristic. Based on the EFSMSG, a mathematical model is formulated for the FPC strategy to minimize task processing delay while facilitating computation during transmission. Finally, the Constraint Selection Adaptive Binary Particle Swarm Optimization (CSABPSO) algorithm is proposed for the solution. Simulation results demonstrate that the proposed FPC strategy effectively reduces task processing delay, meeting the requirements of delay-sensitive tasks in emergency situations.

翻译：在应急无人机网络（EUN）中，将延迟敏感任务卸载至无人机集群可极大提升救援效率。多数任务卸载策略假设无人机位置固定且能处理所有任务。然而，在复杂的灾害环境中，无人机位置常动态变化，且机载资源的异构性对优化EUN中的任务调度以最小化延迟构成了重大挑战。为解决这些问题，本文提出一种基于有限状态机的应急无人机集群路径跟随协同计算策略（FPC）。首先，构建扩展有限状态机时空图（EFSMSG）模型，以精确表征机载资源与状态转换，同时屏蔽EUN的动态特性。基于EFSMSG，为FPC策略建立数学模型，旨在最小化任务处理延迟并促进传输过程中的计算。最后，提出约束选择自适应二进制粒子群优化（CSABPSO）算法进行求解。仿真结果表明，所提出的FPC策略能有效降低任务处理延迟，满足应急场景下延迟敏感任务的需求。