基于行为预测的调度框架，提升无人机与无人车任务执行效率
AG-MPBS: a Mobility-Aware Prediction and Behavior-Based Scheduling Framework for Air-Ground Unmanned Systems
- 结合行为分类与时空预测，动态匹配任务与设备
- 在真实数据集上任务完成率提升显著，资源利用率更高
- 适合城市感知、应急响应等实时性要求高的场景
随着无人机（UAVs）和无人地面车（UGVs）在城市感知和应急响应等应用中日益重要，如何高效调度这些自主设备完成时效性任务成为关键挑战。本文提出MPBS（基于移动性预测与行为调度的框架），将每台设备视为可调度的‘用户’，集成三个核心模块：基于行为的KNN分类器、时变马尔可夫移动性预测模型，以及考虑任务紧迫性与基站性能的动态优先级调度机制。通过融合行为识别与时空预测，MPBS实现任务与设备的实时自适应分配。在真实世界GeoLife数据集上的实验表明，该框架显著提升了任务完成效率与资源利用率。所提方案为无人系统提供了可预测、行为感知的智能协同调度新范式。
原文摘要 · Abstract (English)
As unmanned systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) become increasingly important to applications like urban sensing and emergency response, efficiently recruiting these autonomous devices to perform time-sensitive tasks has become a critical challenge. This paper presents MPBS (Mobility-aware Prediction and Behavior-based Scheduling), a scalable task recruitment framework that treats each device as a recruitable "user". MPBS integrates three key modules: a behavior-aware KNN classifier, a time-varying Markov prediction model for forecasting device mobility, and a dynamic priority scheduling mechanism that considers task urgency and base station performance. By combining behavioral classification with spatiotemporal prediction, MPBS adaptively assigns tasks to the most suitable devices in real time. Experimental evaluations on the real-world GeoLife dataset show that MPBS significantly improves task completion efficiency and resource utilization. The proposed framework offers a predictive, behavior-aware solution for intelligent and collaborative scheduling in unmanned systems.
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