Hardware-Accelerated Geometrical Simulation of Biological and Engineered In-Air Ultrasonic Systems

TL;DR

SonoTraceUE, a high-fidelity acoustic simulation framework built as a plugin for Unreal Engine, provides a versatile platform for synthetic data generation, hypothesis testing in bioacoustics, and the rapid prototyping of closed-loop robotic systems that use acoustic sensing.

Abstract

The deployment of in-air acoustic sensors for industrial monitoring and autonomous robotics has grown significantly, often drawing inspiration from biological echolocation. However, developing and validating these systems in existing simulation frameworks remains challenging due to the computational cost of simulating high-frequency wave propagation in large, dynamic, and complex environments. While wave-based methods offer high accuracy, they scale poorly with frequency and volume. Conversely, existing geometric acoustic solvers often lack support for dynamic scenes, complex diffraction, or closed-loop robotic integration. In this work, we introduce SonoTraceUE, a high-fidelity acoustic simulation framework built as a plugin for Unreal Engine. By using a hardware-accelerated ray tracing-based specular reflection model, and a curvature-based Monte Carlo diffraction model, the system enables near real-time simulation of active and passive acoustic sensing in dynamic, multi-material environments. We validate the framework through two distinct experimental domains: a bioacoustic study and a robotics experiment. Our results demonstrate that SonoTraceUE achieves high correlation with real-world spectral and spatial data. The framework provides a versatile platform for synthetic data generation, hypothesis testing in bioacoustics, and the rapid prototyping of closed-loop robotic systems that use acoustic sensing.

Figures (15)

• Figure 1: The various acoustic phenomena that are modeled in the abstraction model as proposed in this paper. The big omissions are refraction and more complex forms of medium transmission (e.g., for in-water simulation). Specular, diffuse, and absorption are simulated directly using ray tracing. In contrast, diffraction uses a Monte Carlo approximation to the wave equation solution, based on the local curvature calculated from the scene geometry.
• Figure 2: An example of a large forest environment simulated with SonoTraceUE in Unreal Engine, consisting of 96 unique 3D meshes totaling over 10000instances for a combined 21000000triangles. The colored points shown in the image are representative of a small portion of the simulated points by the simulation of a bat, which has two receivers and one emitter in this scenario. A video of the simulation running in this scene is available as supplementary material and online sonotraceueyoutube.
• Figure 3: Diagrams visualizing the architecture of the simulation framework. a) Block diagram showing the sequential stages of the simulation implementation. It shows the three main components: specular, diffraction, and passive sensing, respectively. The impulse response is generated, and the receiver signals are created in third-party software via the TCP API. b) High-level schematic of the proposed system architecture integrated within the Unreal Engine framework. The plugin serves as the main block controlling and executing the simulation pipeline, acting as an intermediary between the host application's CPU thread, which handles rigid-body physics and dynamic scene updates, and the low-level render dependency graph that uses the Rendering Hardware Interface (RHI) of Unreal Engine to access the GPU. Acoustic propagation is offloaded to the GPU via asynchronous compute shaders utilizing DirectX Ray Tracing (DXR) pipelines. Interaction with the simulation and final signal post-processing is available through a TCP API to third-party software, such as MATLAB.
• Figure 4: Comparison of the real-world experimental data of a single frame and its simulated recreation of the pallid bat hunting behavior. a) Three camera views captured simultaneously of the bat approaching its scorpion prey in the real-world experiment. This scorpion is sitting on a flat surface, with the 64-microphone array embedded within it. The acoustic intensity of the echolocation (scan pattern) is shown in a color gradation overlay. b) The same frame recreated with SonoTraceUE in Unreal Engine with the same poses for the microphones, scorpion, and bat. The locations of all 64 microphones are shown with green dots. A close-up of the 3D model used for the scorpion is shown in the top-left corner. c) The normalized scan patterns showing the acoustic intensity across the array. It shows the attention zone of the bat's emitted call. While the location is the same for the real-world and simulated results, the emission directivity influences the visible pattern. d) The spectrogram of the echolocation signals during this measurement frame of the approaching bat, used by the simulation.
• Figure 5: Various visualizations of the 3D scene from the same point of view used during the bioacoustic surface analysis experiment in simulation. The experiment shows how a rough surface affects the acoustic signature of a scorpion during bat echolocation hunting. a) A close-up of the 3D scene as created in Unreal Engine. b) The surface normal is visualized, which is used by the proposed method for calculating the curvature of the 3D meshes. c) The wireframe of the 3D meshes used in the scene showing the triangle-based geometry with the scorpion highlighted in green. d) Only the light rendering result is shown here, where Unreal Engine also uses hardware-accelerated ray tracing to simulate light bouncing throughout the scene.