2025 IJCAI IJCAI 2025

Aerial Coverage Path Planning in Nuclear Emergencies

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.

🌉 Interdisciplinary Bridge — Computer Science and Machine Learning
🧭 Keyword Pioneer — radiation mapping
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio