High Precision Excavator Control

Why It Matters

Centimeter‑accurate autonomous grading across hydraulic platforms cuts rework and boosts equipment utilization, delivering measurable cost savings for contractors.

Key Takeaways

• New autonomous control stack achieves centimeter‑level grading precision.
• Works across load‑sensing and negative‑flow hydraulic architectures.
• Combines hydraulics‑aware joint velocity controller with model predictive path tracking.
• Validated on Menzi Muck M445 and Case 250 excavators.
• Maintains ~1.8 cm accuracy across depths; stalls only at max pressure.

Summary

The video introduces an autonomous control stack designed for heavy‑duty excavator grading that delivers centimeter‑level surface accuracy, addressing the precision loss and torque under‑utilization of existing semi‑automatic systems.

The solution splits into two modules: a hydraulics‑aware joint velocity controller that adapts to both load‑sensing and negative‑flow architectures, and a model predictive controller that coordinates joint motions to follow a design surface. This architecture compensates for pressure‑dependent joint dynamics and fully exploits available torque.

Demonstrations on a Menzi Muck M445 (load‑sensing) and a Case 250 (negative‑flow) show consistent performance. Grading tests achieved roughly 1.8 cm surface error regardless of cutting depth, with stalling observed only at the machine’s maximum function pressure.

The approach promises higher productivity and reduced rework for construction firms, while offering a hardware‑agnostic solution that can be retrofitted to existing fleets, potentially reshaping excavator automation standards.

Original Description

High-precision heavy-duty grading is a common step in earthworks, traditionally carried out manually by skilled operators. Removing a significant amount of material while achieving a high-precision surface requires substantial machine-specific experience. Different hydraulic architectures react differently to operator inputs and soil interaction forces, which makes generalizable controllers challenging. In this paper, we present an autonomous controller that achieves high-precision grading at expert-operator speed on Load Sensing and Negative Flow Control machines alike. We split our controller into two parts: (1) a hydraulic-aware low-level loop that is hydraulic architecture-specific and (2) a path-tracking layer that coordinates joint motions and responses. Through a calibration process, our technique is applicable to load-sensing and negative-flow-control machinery. To showcase its versatility, we benchmark our approach on two excavators with different hydraulics and compare it against a commercial state-of-the-art solution. Our technique (RMSE 1.8 cm) outperforms the commercial solution (RMSE 4.7 cm) in precision by a factor of 2.6 and improves machine usage by leveraging the maximum function pressure, as opposed to commercial solutions that stall prematurely.

Read our paper: https://arxiv.org/abs/2605.09465

Lennart Werner, Pol Eyschen, Sean Costello, Andrei Cramariuc and Marco Hutter