RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine

TL;DR

RESenv is a highly realistic earthquake simulation environment built on Unreal Engine, designed to generate synthetic visual data for AI and robotics in rescue operations, incorporating real seismic data and advanced physics for enhanced realism.

Contribution

The paper introduces RESenv, a novel earthquake simulation system that combines real seismic data with Unreal Engine's physics for realistic, detailed visualizations in complex urban scenarios.

Findings

Outperforms traditional methods in realism and efficiency.

Enables effective AI training for rescue tasks.

Validated through AI tasks like path planning and image segmentation.

Abstract

Earthquakes have a significant impact on societies and economies, driving the need for effective search and rescue strategies. With the growing role of AI and robotics in these operations, high-quality synthetic visual data becomes crucial. Current simulation methods, mostly focusing on single building damages, often fail to provide realistic visuals for complex urban settings. To bridge this gap, we introduce an innovative earthquake simulation system using the Chaos Physics System in Unreal Engine. Our approach aims to offer detailed and realistic visual simulations essential for AI and robotic training in rescue missions. By integrating real seismic waveform data, we enhance the authenticity and relevance of our simulations, ensuring they closely mirror real-world earthquake scenarios. Leveraging the advanced capabilities of Unreal Engine, our system delivers not only high-quality visualisations but also real-time dynamic interactions, making the simulated environments more immersive and responsive. By providing advanced renderings, accurate physical interactions, and comprehensive geological movements, our solution outperforms traditional methods in efficiency and user experience. Our simulation environment stands out in its detail and realism, making it a valuable tool for AI tasks such as path planning and image recognition related to earthquake responses. We validate our approach through three AI-based tasks: similarity detection, path planning, and image segmentation.