Strategies for Decentralized UAV-Based Collision Monitoring in Rugby

Recent advancements in uncrewed 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 sports events for dangerous injuries, we envision a coordinated UAV fleet designed to capture high-quality, multiview video footage of collision events in real time. The extracted video data are crucial for analyzing athletes’ motions and investigating the probability of sports-related traumatic brain injuries (TBIs) during impacts. This article implemented a UAV fleet system on the NetLogo platform, utilizing custom collision observation algorithms to compare against traditional television (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 multistep 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 2-D 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 record 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.