---
title: "Aerial Coverage Path Planning in Nuclear Emergencies"
authors:
  - "Johann Blake"
  - "Matthias Schubert"
doi: "10.24963/ijcai.2025/1251"
url: "https://ijcai.org/proceedings/2025/1251"
pdf: "https://ijcai.org/proceedings/2025/1251.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11009-11012"
track: "Demo Track"
keywords:
  - "Planning and Scheduling: PS: Markov decisions processes"
  - "Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence"
  - "Planning and Scheduling: PS: Planning under uncertainty"
  - "Machine Learning: ML: Reinforcement learning"
---

# Aerial Coverage Path Planning in Nuclear Emergencies

Johann Blake, Matthias Schubert

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11009-11012. DOI: [10.24963/ijcai.2025/1251](https://doi.org/10.24963/ijcai.2025/1251)

## Abstract

We formulate a Coverage Path Planning (CPP) problem for a helicopter or a UAV tasked with mapping ground-level radiation while avoiding radiation that is too strong. We introduce a simulation environment that incorporates digital elevation models, altitude-dependent measurement footprints and realistic flight constraints, as well as state-of-the-art radiation scenario simulations, such as nuclear explosions, provided by the German Federal Office for Radiation Protection. We highlight the complexity of radiological survey missions and demonstrate the necessity for new CPP approaches that address these unique challenges. The code to our simulation environment can be found under https://github.com/JohannBlake/Aerial-Coverage-Path-Planning-in-Nuclear-Emergencies.