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Strategies for decentralised UAV-based collisions monitoring in rugby

Yu Cheng, Harun Šiljak

TL;DR

This paper tackles the challenge of real-time, non-contact monitoring of head collisions in rugby to mitigate traumatic brain injuries. It introduces a decentralized UAV swarm framework evaluated with NetLogo simulations, contrasting six drone operating modes (Fixed, Follow-Ball, Follow-Players, Density-Based, Repulsive, Random) across fleet sizes, speeds, and sensor radii. The results show that dynamic strategies, especially Follow-Players and Density-Based, achieve the highest collision-detection accuracy, with a mid-sized fleet (around 12 drones) offering a favorable balance between accuracy and practicality. The study also develops a drone power-consumption model and discusses extensions to 3D environments and multi-UAV data fusion, highlighting the potential for real-world deployment and further optimization of UAV-based sports safety monitoring.

Abstract

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.

Strategies for decentralised UAV-based collisions monitoring in rugby

TL;DR

This paper tackles the challenge of real-time, non-contact monitoring of head collisions in rugby to mitigate traumatic brain injuries. It introduces a decentralized UAV swarm framework evaluated with NetLogo simulations, contrasting six drone operating modes (Fixed, Follow-Ball, Follow-Players, Density-Based, Repulsive, Random) across fleet sizes, speeds, and sensor radii. The results show that dynamic strategies, especially Follow-Players and Density-Based, achieve the highest collision-detection accuracy, with a mid-sized fleet (around 12 drones) offering a favorable balance between accuracy and practicality. The study also develops a drone power-consumption model and discusses extensions to 3D environments and multi-UAV data fusion, highlighting the potential for real-world deployment and further optimization of UAV-based sports safety monitoring.

Abstract

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.

Paper Structure

This paper contains 25 sections, 25 equations, 15 figures, 12 tables, 6 algorithms.

Table of Contents

  1. Background Introduction
  2. Related work
  3. System Overview
  4. Model Description
  5. The Rugby Model
  6. Field Setup
  7. Players Initialization
  8. Ball Mechanics
  9. Game Mechanics and Player Interaction Dynamics
  10. The Drone Model Algorithms
  11. Fixed Mode Algorithm
  12. Follow-Ball Mode Algorithm
  13. Repulsive Mode Algorithm
  14. Follow-Players Mode Algorithm
  15. Density-Based Mode Algorithm
  16. ...and 10 more sections

Figures (15)

  • Figure 1: The general framework of our model
  • Figure 2: Flowchart of Game Mechanics and Collision Detection with Risk Assessment
  • Figure 3: Generated Heatmap Based on Collision Data
  • Figure 4: Simulation Results of Fixed UAV Positions (Fixed Mode) with 2 Drones in the radius of 5
  • Figure 5: Flowchart for Identifying High-Risk Players and Drone Assignment
  • ...and 10 more figures