用无人机群实时捕捉橄榄球碰撞，提升脑损伤预测精度。
Strategies for decentralised UAV-based collisions monitoring in rugby
- 无人机群自主协同，通过定制算法实时检测碰撞。
- 相比传统电视转播，数据捕获更精准及时，提升脑损伤预测能力。
- 适合体育安全研究与智能监控系统开发人员参考。
无人飞行器（UAV）技术的进步为复杂环境下的动态数据采集开辟了新途径，如快速运动的体育赛事中。为监测可能导致严重伤害的碰撞事件，本文设计了一个协同无人机群系统，旨在实时获取高质量、多视角的碰撞视频。这些视频数据对分析运动员动作及评估运动相关创伤性脑损伤（TBI）概率至关重要。研究在NetLogo平台上实现无人机群系统，采用自定义碰撞检测算法，与传统电视转播策略进行对比。系统支持去中心化数据采集与自主处理，具备应对体育碰撞动态变化的鲁棒性。协作算法融合共享与本地数据，进行多步分析，以评估定制方法在提升TBI预测模型准确性方面的有效性。任务在二维模型中实时模拟，聚焦于可能引发TBI的碰撞事件捕捉，同时考虑快速机动与最优定位等操作约束。初步仿真结果表明，自定义碰撞检测方法在精确性和时效性上优于标准电视覆盖策略，凸显了定制算法在关键体育安全应用中的优势。
原文摘要 · Abstract (English)
Recent advancements in unmanned aerial vehicle (UAV) technology have opened new avenues for dynamic data collection in challenging environments, such as sports fields during fast-paced sports action. For the purposes of monitoring sport events for dangerous injuries, we envision a coordinated UAV fleet designed to capture high-quality, multi-view video footage of collision events in real-time. The extracted video data is crucial for analyzing athletes' motions and investigating the probability of sports-related traumatic brain injuries (TBI) during impacts. This research implemented a UAV fleet system on the NetLogo platform, utilizing custom collision detection algorithms to compare against traditional TV-coverage strategies. Our system supports decentralized data capture and autonomous processing, providing resilience in the rapidly evolving dynamics of sports collisions. The collaboration algorithm integrates both shared and local data to generate multi-step analyses aimed at determining the efficacy of custom methods in enhancing the accuracy of TBI prediction models. Missions are simulated in real-time within a two-dimensional model, focusing on the strategic capture of collision events that could lead to TBI, while considering operational constraints such as rapid UAV maneuvering and optimal positioning. Preliminary results from the NetLogo simulations suggest that custom collision detection methods offer superior performance over standard TV-coverage strategies by enabling more precise and timely data capture. This comparative analysis highlights the advantages of tailored algorithmic approaches in critical sports safety applications.
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