Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Autonomous leveling of granular materials is a ubiquitous yet challenging operationin automated construction due to the complex physics governing the soil–tool interaction. This paper outlines a simulation‐driven framework for optimizing low‐level bulldozer blade control (pitch and height) to enhance leveling performance. The approach uses high‐fidelity, physics‐based simulations to generate training data. This data informs a neural network based reduced‐order model that accurately predicts both the terrain evolution and the leveling operation duration in response to blade actions. A gradient‐based, multiobjective optimization algorithm then utilizes the reduced‐order model to determine optimal, time‐varying blade control profiles, managing the trade‐off between leveling flatness and operation time. The proposed method augments the state‐of‐the‐art by producing policies that can readily level arbitrary soil pile configurations while avoiding vehicle immobilization and achieving better leveling efficiency. The system exhibits robustness to variations in initial pile geometry, and offers explicit control over the trade‐off between leveling quality and operational efficiency. By integrating high‐fidelity physics into the controller design and providing an open‐source simulation pipeline, this work provides a low‐level control solution that complements existing global path planning algorithms for autonomous construction operations. The project's resources, including code and media demonstration, are available at: https://uwsbel.github.io/Autonomous‐Leveling/ .