An online estimation algorithm to predict external forces acting on a front-end loader

Abstract

One of the most significant goals of earthmoving equipment is to maximize productivity during loading cycles. A real-time knowledge of the forces exchanged between the machine implement and the surrounding, that is, while digging, can be used in different ways to increase productivity. It can be used to determine the amount of material moved by the machine; or to find the optimal bucket trajectory; moreover, as input to traction control systems. This article presents an online force estimation algorithm able to predict vertical and horizontal forces exchanged between the front-loader and the surrounding environment, as well as the reaction forces through the implement itself. Taking the case of a 14-ton wheel loader as reference, this article illustrates the development of a simulation model for the analysis of the machine digging system, along with the instrumentation and testing of the proposed estimation algorithm. The model is divided into two sections describing, respectively, system kinematic and system dynamics. The kinematic model of the front-loader is compared against measurements, and results show an average error lower than 1%. The dynamic model predicts both hydraulic and dynamic features of the machine implement, achieving an accuracy on the payload mass within 2%–3%, even during dynamic conditions. The estimated pushing force reflects the expected behavior when tested for various pushing efforts and in different ground conditions. Eventually, the algorithm was tested on a complete loading cycle and the results show good consistency considering the measured front-loader trajectory and vehicle speed. The proposed model overcomes some drawbacks of the currently used technologies. For example, it allows for an online estimation of the bucket forces for any position of the implement. Although the results discussed in this article pertain to a specific reference machine, the proposed method can be extended to most wheel loaders equipped with standard digging equipment.

Keywords

Bucket force estimation digging forces payload estimation wheel loader resistance forces

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References

Schexnayder

Weber

Brooks

Effect of truck payload weight on production. J Constr Eng M 1999; 125(1): 1–7.

Alexander

Vacca

Longitudinal vehicle dynamics model for construction machines with experimental validation. Int J Automot Mech Eng 2017; 14: 4616–4633.

Spencer

Wullt

Tire and force distribution modeling and validation for wheel loader applications. Master’s Thesis, Luleå University of Technology, Luleå, 2017.

Tiwari

Knowles

Danko

Bucket trajectory classification of mining excavators. Automat Constr 2013; 31: 128–139.

Kim

Kang

, et al. Dynamically optimal trajectories for earthmoving excavators. Automat Constr 2013; 35: 568–578.

Lee

Kim

. Trajectory generation for an automated excavator. In: 2014 14th international conference on control, automation and systems (ICCAS 2014), Gyeonggi-do, South Korea, 22–25 October 2014, pp.716–719. New York: IEEE.

Hemami

Motion trajectory study in the scooping operation of an LHD-loader. IEEE T Ind Appl 1994; 30: 1333–1338.

Sarata

Osumi

Hirai

, et al. Trajectory arrangement of bucket motion of wheel loader. In: 2003 proceedings of the 20th ISARC, Eindhoven, 21–24 September 2003, pp.135–140, https://www.iaarc.org/publications/fulltext/isarc2003-77.pdf

Takahashi

Hasegawa

Nakano

Analysis on the resistive forces acting on the bucket of a Load-Haul-Dump machine and a wheel loader in the scooping task. Adv Robotics 1998; 13: 97–114.

10.

Frank

Kleinertc

Filla

Optimal control of wheel loader actuators in gravel applications. Automat Constr 2018; 91: 1–14.

11.

Sarata

Osumi

Kawai

, et al. Trajectory arrangement based on resistance force and shape of pile at scooping motion. In: IEEE international conference on robotics and automation, 2004. proceedings (ICRA ’04), vol. 4, New Orleans, LA, 26 April–1 May 2004, pp.3488–3493. New York: IEEE.

12.

Bodur

Zontul

Ersak

, et al. Dynamic cognitive force control for an automatic land excavation robot. In: Proceedings of MELECON ‘94 Mediterranean electrotechnical conference, vol. 2, Antalya, Turkey, 12–14 April 1994, pp.703–706. New York: IEEE.

13.

Richardson-Little

Damaren

. Position accommodation and compliance control for robotic excavation. In: Proceedings of 2005 IEEE conference on control applications (CCA 2005), Toronto, ON, Canada, 28–31 August 2005, pp.1194–1199. New York: IEEE.

14.

Patel

Prajapati

Soil-tool interaction as a review for digging operation of mini hydraulic excavator. Int J Eng Sci Tech 2011; 3: 894–901.

15.

Tan

Zweiri

Althoefer

, et al. Online soil-bucket interaction identification for autonomous excavation. In: Proceedings of the 2005 IEEE international conference on robotics and automation, Barcelona, 18–22 April 2005, pp.3576–3581. New York: IEEE.

16.

Nezami

Hashash

Zhao

, et al. Simulation of front end loader bucket-soil interaction using discrete element method. Int J Numer Anal Met 2007; 31(9): 1147–1162.

17.

Luengo

Singh

Cannon

. Modeling and identification of soil-tool interaction in automated excavation. In: Proceedings of the 1998 IEEE/RSJ international conference on intelligent robots and systems. Innovations in theory, practice and applications (Cat. No. 98CH36190), vol. 3, Victoria, BC, Canada, 17 October 1998, pp.1900–1906. New York: IEEE.

18.

Yung

Freidovich

Vazquez

Payload estimation in front-end loaders. In: MCG 2016—5th international conference on machine control & guidance, Vichy, 5–6 October 2016.

19.

Stanley

Buettner

JE.

Vehicle payload weight display method and system. US9157215B2 Patent, 2015.

20.

Hsu

King

Corder

Weight estimation for excavator payloads. US8271229 Patent, 2012.

21.

Bennett

Walawalkar

Schindler

. Payload estimation in excavators: model-based evaluation and comparison of current payload estimation systems. In: CVT2014 3rd commercial vehicle technology symposium, vol. 3, Kaiserslautern, 11–13 March 2014.

22.

Ascorel MC402, http://www.ascorel.com/en/onboard-weighing/174-mc-402

23.

Kang

Jung

Lee

. Modeling and measurement of payload mass of the wheel loader in the dynamic state based on experimental parameter identification. SAE technical paper 2016-01-0469, 2016.

24.

Shatters

Method and system for estimating payload weight with tilt position compensation. US9464403B2 Patent, 2016.

25.

Hindman

Dynamic payload estimation in four wheel drive loaders. PhD Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 2008.

26.

Savia

Koivo

Neural-network-based payload determination of a moving loader. Control Eng Pract 2004; 12(5): 555–561.

27.

Worley

Saponara

VL.

A simplified dynamic model for front-end loader design. Proc IMechE, Part C: J Mechanical Engineering Science 2008; 222(11): 2231–2249.

28.

Takahashi

Yasuhara

Kanai

, et al. Development of bucket scooping mechanism for analysis of reaction force against rock piles. In: 2006 Proceedings of the 23rd ISARC, Tokyo, Japan, 3–5 October 2006, pp.476–481, https://www.iaarc.org/publications/fulltext/isarc2006-00031_200606060835.pdf

29.

Palomba

Richiedei

Trevisani

, et al. Estimation of the digging and payload forces in excavators by means of state observers. Mech Syst Signal Pr 2019; 134: 106356.