arxiv: 2605.09465 · v1 · submitted 2026-05-10 · 💻 cs.RO

Recognition: no theorem link

High Precision Hydraulic Excavator Control for Heavy-Duty Grading

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

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:24 UTC · model grok-4.3

classification 💻 cs.RO

keywords excavator controlautonomous gradinghydraulic controlpath trackingload sensingnegative flow controlprecision earthworksrobotics

0 comments

The pith

A split autonomous controller achieves 1.8 cm grading precision on different hydraulic excavators by adapting low-level loops through calibration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to demonstrate that autonomous systems can perform high-precision heavy-duty grading at the speed of expert human operators, a task that normally demands machine-specific experience because hydraulics and soil forces interact differently on each excavator. It does so by separating the problem into a hydraulic-architecture-specific low-level loop and a higher-level path-tracking layer that coordinates joint motions, then using a calibration step to make the same software work on both load-sensing and negative-flow-control machines. The approach is tested on two real excavators against a commercial controller. A sympathetic reader would care because successful automation here would allow consistent surface quality with less reliance on scarce skilled operators and better utilization of machine power. If the central claim holds, generalizable high-precision grading becomes possible without building a full custom model for every new machine or site condition.

Core claim

The authors claim that a two-part controller consisting of a hydraulic-aware low-level loop tailored to the machine's architecture and a coordinating path-tracking layer, adapted via calibration, delivers autonomous heavy-duty grading at expert speed on both load-sensing and negative-flow-control excavators, producing 1.8 cm RMSE versus 4.7 cm RMSE for the commercial baseline while maintaining maximum function pressure instead of stalling.

What carries the argument

The hydraulic-aware low-level loop, which is made specific to each excavator's hydraulic architecture and tuned through calibration to account for machine dynamics and soil interaction.

Load-bearing premise

A calibration process can adapt the low-level hydraulic loop to new machine architectures and soil conditions without leaving large unmodeled effects that would degrade performance.

What would settle it

Running the calibrated controller on a third excavator or different soil and measuring grading RMSE at or above the commercial 4.7 cm level, or observing premature stalling under load.

Figures

Figures reproduced from arXiv: 2605.09465 by Andrei Cramariuc, Lennart Werner, Marco Hutter, Pol Eyschen, Sean Costello.

**Figure 2.** Figure 2: Flow graph of the grading controller and split between path tracking [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: FF data collection for the Boom joint. Normalized commands from [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Hydraulic FF model of the LS M445. LS enables a direct mapping [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Flow chart of the FF element for NFC hydraulics. Inertia compen [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 9.** Figure 9: Contribution of calibrated NFC hydraulics FF model. PID controller [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗

**Figure 10.** Figure 10: CASE250, Commercial Controller: Evaluation of 10 grading passes [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗

**Figure 13.** Figure 13: Experiment Setup for Surface Quality Measurements on CASE250. [PITH_FULL_IMAGE:figures/full_fig_p008_13.png] view at source ↗

**Figure 12.** Figure 12: M445, Our Controller: Evaluation of 10 grading passes of varying [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗

**Figure 16.** Figure 16: Simplified schematic of a single hydraulic function in an NFC [PITH_FULL_IMAGE:figures/full_fig_p011_16.png] view at source ↗

**Figure 17.** Figure 17: Broken down schematic of NFC for modeling. Highlighted in blue [PITH_FULL_IMAGE:figures/full_fig_p011_17.png] view at source ↗

**Figure 15.** Figure 15: Simplified schematic of a LS hydraulic function. The pump is [PITH_FULL_IMAGE:figures/full_fig_p011_15.png] view at source ↗

**Figure 18.** Figure 18: Menzi Muck M445 [PITH_FULL_IMAGE:figures/full_fig_p012_18.png] view at source ↗

**Figure 19.** Figure 19: CASE250 to the excavator through CAN-Bus. Inertial, joint position and joint velocity data is measured by a Leica Icon Machine Control system. CASE250 in [PITH_FULL_IMAGE:figures/full_fig_p012_19.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives a split controller for excavator grading that beats a commercial baseline by 2.6x in RMSE on two real machines with different hydraulics, but the calibration details and soil robustness are not quantified in the abstract.

read the letter

The main point from this paper is a practical split-controller design for autonomous excavator grading that achieves higher precision than commercial systems on real machines with different hydraulic setups. They split the controller into a hydraulic-aware low-level loop tailored to the specific architecture and a path-tracking layer on top. A calibration process makes it work for both load-sensing and negative-flow-control machines. The benchmarks on two excavators show 1.8 cm RMSE compared to 4.7 cm for the commercial solution, which is a solid factor of improvement, and they note better use of machine pressure without stalling. This is new in combining the architecture-specific low level with a general path tracker and validating it across hydraulic types on hardware. The real-machine testing and direct commercial comparison are strengths here. The soft spots are around the calibration and robustness. The abstract says it works through calibration but gives no numbers on how many parameters, what data is used for calibration, trial counts, or performance under different soil conditions. That leaves the claim of reliable adaptation without extensive per-machine modeling open to question, especially if soil forces vary. This is for researchers and engineers focused on heavy-duty robotics and construction automation. Readers looking for applied control methods on excavators will find the results useful. It deserves a serious referee because the hardware validation is concrete and the improvement is measurable. I would recommend sending it for peer review, though the experimental section will likely need expansion on calibration details and soil variation tests.

Referee Report

1 major / 1 minor

Summary. The paper presents an autonomous controller for high-precision heavy-duty grading on hydraulic excavators. It splits the system into a hydraulic-architecture-specific low-level loop (for Load-Sensing and Negative-Flow-Control machines) and a path-tracking layer that coordinates joint motions. The approach relies on a calibration process to generalize across machines and is benchmarked on two excavators against a commercial baseline, claiming 1.8 cm RMSE (vs. 4.7 cm) for a 2.6x precision improvement while better utilizing maximum function pressure without premature stalling.

Significance. If the experimental claims hold under scrutiny, this would represent a meaningful advance in practical autonomous construction robotics by offering a calibratable controller that works across common hydraulic architectures without extensive per-machine modeling. The physical experiments on two distinct excavators and direct comparison to a commercial state-of-the-art solution are clear strengths that ground the work in real-world applicability.

major comments (1)

[§5 (Benchmarking and Results)] §5 (Benchmarking and Results): The reported RMSE values (1.8 cm vs. 4.7 cm) and factor-of-2.6 outperformance are presented without details on trial count, statistical tests, specific soil conditions, or the calibration procedure (inputs/outputs, number of free parameters, or how soil interaction forces are incorporated). This is load-bearing for the central claim of reliable adaptation via calibration to different machines and conditions without significant unmodeled dynamics.

minor comments (1)

[Abstract] Abstract: The claim of operating at 'expert-operator speed' is not supported by any quantitative comparison to human operators or metrics on cycle time.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment below and will revise the manuscript to provide the requested experimental details.

read point-by-point responses

Referee: The reported RMSE values (1.8 cm vs. 4.7 cm) and factor-of-2.6 outperformance are presented without details on trial count, statistical tests, specific soil conditions, or the calibration procedure (inputs/outputs, number of free parameters, or how soil interaction forces are incorporated). This is load-bearing for the central claim of reliable adaptation via calibration to different machines and conditions without significant unmodeled dynamics.

Authors: We agree that additional details on the experimental protocol are needed to fully support the central claims. In the revised manuscript, Section 5 will be expanded to report the total number of trials (including breakdown by machine and condition), the statistical analysis performed on the RMSE values (e.g., mean, standard deviation, and any hypothesis testing), a description of the soil conditions and types used during testing, and a full account of the calibration procedure specifying its inputs and outputs, the number of free parameters, and how soil interaction forces are incorporated or compensated. These additions will strengthen the evidence for reliable cross-machine adaptation. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents a control architecture for autonomous excavator grading that splits into a hydraulic-architecture-specific low-level loop and a coordinating path-tracking layer, with applicability to different machines achieved via an unspecified calibration process. Validation consists of physical experiments on two excavators (Load-Sensing and Negative-Flow-Control) with direct RMSE comparison to a commercial baseline. No mathematical derivation, equations, or predictive steps are described in the provided text that reduce claimed performance to self-defined quantities, fitted parameters renamed as predictions, or self-citation chains. The contribution is self-contained as an empirical engineering result.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions in hydraulic control and the effectiveness of calibration to handle machine variability; no new entities are postulated.

free parameters (1)

calibration parameters
The paper states that a calibration process adapts the low-level loop to specific hydraulics, implying fitted parameters for gains or models.

axioms (1)

domain assumption Hydraulic dynamics of load-sensing and negative-flow-control systems can be adequately captured by an architecture-specific low-level controller after calibration
This is invoked when splitting the controller and claiming applicability to both machine types.

pith-pipeline@v0.9.0 · 5505 in / 1370 out tokens · 76477 ms · 2026-05-12T04:24:18.132245+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

[1]

Examining the simulation-to-reality gap of a wheel loader digging in de- formable terrain.Multibody System Dynamics, July 2024

Koji Aoshima and Martin Servin. Examining the simulation-to-reality gap of a wheel loader digging in de- formable terrain.Multibody System Dynamics, July 2024. ISSN 1573-272X. doi: 10.1007/s11044-024-10005-5

work page doi:10.1007/s11044-024-10005-5 2024
[2]

Earth- moving equipment automation: a review of technical advances and future outlook.Tcon, 22:247–265

Ehsan Rezazadeh Azar and Vineet R Kamat. Earth- moving equipment automation: a review of technical advances and future outlook.Tcon, 22:247–265. URL https://www.itcon.org/2017/13

work page 2017
[3]

Cat grade for excavators

Caterpillar. Cat grade for excavators. https://www.cat.co m/en US/products/new/technology/grade/grade/159698 04.html. Accessed: 2025-11-10

work page 2025
[4]

Active Distur- bance Rejection Contouring Control of Robotic Excava- tors with Output Constraints and Sliding Mode Observer

Hoang Vu Dao and Kyoung Kwan Ahn. Active Distur- bance Rejection Contouring Control of Robotic Excava- tors with Output Constraints and Sliding Mode Observer. Applied Sciences, 12(15):7453, 2022. ISSN 2076-3417. doi: 10.3390/app12157453

work page doi:10.3390/app12157453 2022
[5]

Hoang Vu Dao, Seonjun Na, Duc Giap Nguyen, and Ky- oung Kwan Ahn. High accuracy contouring control of an excavator for surface flattening tasks based on extended state observer and task coordinate frame approach.Au- tomation in Construction, 130:103845, October 2021. ISSN 0926-5805. doi: 10.1016/j.autcon.2021.103845

work page doi:10.1016/j.autcon.2021.103845 2021
[6]

Dell, editor.Hydraulic Systems for Mobile Equipment

Timothy W. Dell, editor.Hydraulic Systems for Mobile Equipment. G-W Publisher, 2023

work page 2023
[7]

A General Approach for the Automation of Hydraulic Excavator Arms Using Reinforcement Learning.IEEE Robotics and Automation Letters, 7(2):5679–5686, April 2022

Pascal Egli and Marco Hutter. A General Approach for the Automation of Hydraulic Excavator Arms Using Reinforcement Learning.IEEE Robotics and Automation Letters, 7(2):5679–5686, April 2022. ISSN 2377-3766. doi: 10.1109/LRA.2022.3152865

work page doi:10.1109/lra.2022.3152865 2022
[8]

Re- inforcement Learning-Based Bucket Filling for Au- tonomous Excavation.IEEE Transactions on Field Robotics, 1:170–191, 2024

Pascal Egli, Lorenzo Terenzi, and Marco Hutter. Re- inforcement Learning-Based Bucket Filling for Au- tonomous Excavation.IEEE Transactions on Field Robotics, 1:170–191, 2024. doi: 10.1109/TFR.2024.3 432508

work page doi:10.1109/tfr.2024.3 2024
[9]

Leica mc1

Leica Geosystems. Leica mc1. https://leica-geosystems. com/en-us/products/machine-control-systems/software/ leica-mc1. Accessed: 2025-11-10

work page 2025
[10]

Towards Learning Boulder Excavation with Hydraulic Excavators

Jonas Gruetter, Lorenzo Terenzi, Pascal Egli, and Marco Hutter. Towards Learning Boulder Excavation with Hydraulic Excavators. 2025. doi: 10.48550/arXiv.2 509.17683

work page doi:10.48550/arxiv.2 2025
[11]

Trimble earthworks

Trimble Inc. Trimble earthworks. https://heavyindus try.trimble.com/en/products/grade-control-excavators. Accessed: 2025-11-10

work page 2025
[12]

Inkyu Jang, Junha Kim, Dongjae Lee, Changhyeon Kim, Changsuk Oh, Youngbum Kim, Sangwook Woo, Heejee Sung, and H. Jin Kim. Towards Fully Integrated Au- tonomous Excavation: Autonomous Excavator for Pre- cise Earth Cutting and Onboard Landscape Inspection. IEEE Robotics & Automation Magazine, 32(3):88–102,

work page
[13]

doi: 10.1109/MRA.2024.3400772

work page doi:10.1109/mra.2024.3400772 2024
[14]

Learning Excavation of Rigid Objects with Offline Reinforcement Learning

Shiyu Jin, Zhixian Ye, and Liangjun Zhang. Learning Excavation of Rigid Objects with Offline Reinforcement Learning. 2023. doi: 10.48550/arXiv.2303.16427

work page doi:10.48550/arxiv.2303.16427 2023
[15]

Zeilinger

Dominic Jud, Philipp Leemann, Simon Kerscher, and Marco Hutter. Autonomous Free-Form Trenching Using a Walking Excavator.IEEE Robotics and Automation Letters, 4(4):3208–3215, 2019. doi: 10.1109/LRA.2019 .2925758

work page doi:10.1109/lra.2019 2019
[16]

Modeling and velocity-field control of autonomous excavator with main control valve.Au- tomatica, 104:67–81, 2019

Kwangmin Kim, Minji Kim, Dongmok Kim, and Dongjun Lee. Modeling and velocity-field control of autonomous excavator with main control valve.Au- tomatica, 104:67–81, 2019. ISSN 0005-1098. doi: 10.1016/j.automatica.2019.02.041

work page doi:10.1016/j.automatica.2019.02.041 2019
[17]

Enhanced Hydraulic Excavator Control via Semi-automatic Grading Control Using Reinforcement Learning.International Journal of Control, Automation and Systems, 23(3):896–906, 2025

Youngbum Kim and Jinwhan Kim. Enhanced Hydraulic Excavator Control via Semi-automatic Grading Control Using Reinforcement Learning.International Journal of Control, Automation and Systems, 23(3):896–906, 2025. ISSN 2005-4092. doi: 10.1007/s12555-024-0213-9

work page doi:10.1007/s12555-024-0213-9 2025
[18]

Contour control for leveling work with robotic excavator.Inter- national Journal of Precision Engineering and Manufac- turing, 14(12):2055–2060, December 2013

Chang Seop Lee, Jangho Bae, and Daehie Hong. Contour control for leveling work with robotic excavator.Inter- national Journal of Precision Engineering and Manufac- turing, 14(12):2055–2060, December 2013. ISSN 2005-

work page 2055
[19]

doi: 10.1007/s12541-013-0278-5

work page doi:10.1007/s12541-013-0278-5
[20]

Precision Motion Control of Robotized Industrial Hydraulic Exca- vators via Data-Driven Model Inversion.IEEE Robotics and Automation Letters, 7(2):1912–1919, April 2022

Minhyeong Lee, Hyelim Choi, ChangU Kim, Jihyun Moon, Dongmok Kim, and Dongjun Lee. Precision Motion Control of Robotized Industrial Hydraulic Exca- vators via Data-Driven Model Inversion.IEEE Robotics and Automation Letters, 7(2):1912–1919, April 2022. ISSN 2377-3766. doi: 10.1109/LRA.2022.3142389

work page doi:10.1109/lra.2022.3142389 1912
[21]

Review of Flow-Matching Technology for Hydraulic Systems.Processes, 10(12): 2482, December 2022

Ruichuan Li, Qiyou Sun, Xinkai Ding, Yisheng Zhang, Wentao Yuan, and Tong Wu. Review of Flow-Matching Technology for Hydraulic Systems.Processes, 10(12): 2482, December 2022. ISSN 2227-9717. doi: 10.3390/ pr10122482

work page 2022
[22]

Exca- vation Reinforcement Learning Using Geometric Rep- resentation.IEEE Robotics and Automation Letters, 7 (2):4472–4479, April 2022

Qingkai Lu, Yifan Zhu, and Liangjun Zhang. Exca- vation Reinforcement Learning Using Geometric Rep- resentation.IEEE Robotics and Automation Letters, 7 (2):4472–4479, April 2022. ISSN 2377-3766. doi: 10.1109/LRA.2022.3150511

work page doi:10.1109/lra.2022.3150511 2022
[23]

Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks

Salim Msaad, Leonardo Cecchin, Ozan Demir, and Lorenzo Fagiano. Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks. In 2025 American Control Conference (ACC), page 85–90,

work page 2025
[24]

doi: 10.23919/ACC63710.2025.11107493

work page doi:10.23919/acc63710.2025.11107493 2025
[25]

Huynh A. D. Nguyen and Quang P. Ha. Robotic au- tonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey. Robotica, 41(2):486–510, February 2023. ISSN 0263- 5747, 1469-8668. doi: 10.1017/S0263574722000339

work page doi:10.1017/s0263574722000339 2023
[26]

Zhao, Chenfeng Xu, Chen Tang, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, and Wei Zhan

Filippo A. Spinelli, Pascal Egli, Julian Nubert, Fang Nan, Thilo Bleumer, Patrick Goegler, Stephan Brockes, Ferdinand Hofmann, and Marco Hutter. Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools.Inter- national Conference on Intelligent Robots and Systems, page 12694–12701, October 2024. ISSN 2153-...

work page doi:10.1109/iros58592.2024.10802199 2024
[27]

Negative flow control vs load sensing - which one is better? https://www.insanehydraulics.com /letstalk/nfcvsls.html, 2025

Sergiy Sydorenko. Negative flow control vs load sensing - which one is better? https://www.insanehydraulics.com /letstalk/nfcvsls.html, 2025. Accessed: 2025-12-04

work page 2025
[28]

Toward Autonomous Excavation Planning.IEEE Transactions on Field Robotics, 1:292–317, 2024

Lorenzo Terenzi and Marco Hutter. Toward Autonomous Excavation Planning.IEEE Transactions on Field Robotics, 1:292–317, 2024. ISSN 2997-1101. doi: 10.1109/TFR.2024.3485037

work page doi:10.1109/tfr.2024.3485037 2024
[29]

Robotic excavator motion control using a nonlinear proportional-integral controller and cross-coupled pre-compensation.Automation in Con- struction, 64:1–6, April 2016

Dongyun Wang, Lijuan Zheng, Hongxiang Yu, Wu Zhou, and Liping Shao. Robotic excavator motion control using a nonlinear proportional-integral controller and cross-coupled pre-compensation.Automation in Con- struction, 64:1–6, April 2016. ISSN 09265805. doi: 10.1016/j.autcon.2015.12.024

work page doi:10.1016/j.autcon.2015.12.024 2016
[30]

Observer-Based Approximate Affine Nonlinear Model Predictive Controller for Hydraulic Robotic Excavators with Constraints.Processes, 11(7), 2023

Jian Wang, Hao Zhang, Peng Hao, and Hua Deng. Observer-Based Approximate Affine Nonlinear Model Predictive Controller for Hydraulic Robotic Excavators with Constraints.Processes, 11(7), 2023. ISSN 2227-

work page 2023
[31]

Multidisciplinary Digital Publishing Institute

doi: 10.3390/pr11071918. Multidisciplinary Digital Publishing Institute

work page doi:10.3390/pr11071918
[32]

Efficiency analysis and evaluation of energy-saving pressure-compensated circuit for hybrid hydraulic excavator.Automation in Construction, 47:62–68, November 2014

Tao Wang and Qingfeng Wang. Efficiency analysis and evaluation of energy-saving pressure-compensated circuit for hybrid hydraulic excavator.Automation in Construction, 47:62–68, November 2014. ISSN 0926-

work page 2014
[33]

doi: 10.1016/j.autcon.2014.07.012

work page doi:10.1016/j.autcon.2014.07.012 2014
[34]

Zhao, Chenfeng Xu, Chen Tang, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, and Wei Zhan

Lennart Werner, Fang Nan, Pol Eyschen, Filippo A. Spinelli, Hongyi Yang, and Marco Hutter. Dynamic Throwing with Robotic Material Handling Machines. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 98–104, 2024. doi: 10.1109/IROS58592.2024.10802743

work page doi:10.1109/iros58592.2024.10802743 2024
[35]

Calibrated dynamic mod- eling for force and payload estimation in hydraulic machinery.Construction Robotics, 9(2), October 2025

Lennart Werner, Pol Eyschen, Sean Costello, Pierluigi Micarelli, and Marco Hutter. Calibrated dynamic mod- eling for force and payload estimation in hydraulic machinery.Construction Robotics, 9(2), October 2025. ISSN 2509-8780. doi: 10.1007/s41693-025-00169-7

work page doi:10.1007/s41693-025-00169-7 2025
[36]

Au- tomated Grading Operation for Hydraulic Excavators

Jiaqi Xu, Bradley Thompson, and Hwan-Sik Yoon. Au- tomated Grading Operation for Hydraulic Excavators. InSAE Technical Paper 2014-01-2405, 2014. doi: 10.4271/2014-01-2405

work page doi:10.4271/2014-01-2405 2014
[37]

Mo- tion control for earth excavation robot based on force pre-load and cross-coupling compensation.Automation in Construction, 141:104402, 2022

Teng Yang, Bin Zhang, Haocen Hong, Yuanlong Chen, Huayong Yang, Tongman Wang, and Donghui Cao. Mo- tion control for earth excavation robot based on force pre-load and cross-coupling compensation.Automation in Construction, 141:104402, 2022. ISSN 09265805. doi: 10.1016/j.autcon.2022.104402

work page doi:10.1016/j.autcon.2022.104402 2022
[38]

An autonomous excavator system for material loading tasks.Science Robotics, 6(55): eabc3164, 2021

Liangjun Zhang, Jinxin Zhao, Pinxin Long, Liyang Wang, Lingfeng Qian, Feixiang Lu, Xibin Song, and Dinesh Manocha. An autonomous excavator system for material loading tasks.Science Robotics, 6(55): eabc3164, 2021. doi: 10.1126/scirobotics.abc3164

work page doi:10.1126/scirobotics.abc3164 2021
[39]

Optimum settings for automatic controllers.Transactions of the American society of mechanical engineers, 64(8):759– 765, 1942

John G Ziegler and Nathaniel B Nichols. Optimum settings for automatic controllers.Transactions of the American society of mechanical engineers, 64(8):759– 765, 1942. APPENDIXA HYDRAULICBACKGROUND A. Load Sensing Hydraulic Model The simplified load-sensing circuit in Figure 15 shows the pressure feedback loop of an LS function. The pump senses the pressur...

work page 1942