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Technical Brief

Model-Based Control of Three Degrees of Freedom Robotic Bulldozing

[+] Author and Article Information
Scott G. Olsen

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON, L8S 4L7, Canada
e-mail: olsensg@gmail.com

Gary M. Bone

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON, L8S 4L7, Canada
e-mail: gary@mcmaster.ca

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC Systems, MEASUREMENT, AND CONTROL. Manuscript received June 11, 2012; final manuscript received October 24, 2013; published online December 2, 2013. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 136(2), 024502 (Dec 02, 2013) (5 pages) Paper No: DS-12-1185; doi: 10.1115/1.4025861 History: Received June 11, 2012; Revised October 24, 2013

This brief paper investigates the control of a robotic bulldozing operation. Optimal blade position control laws were designed based on a hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. Experiments were conducted with a scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the best optimal controller demonstrated a 33% increase in the average material removal rate compared to the rule-based controller.

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References

Ito, N., 1991, “Bulldozer Blade Control,” J. Terramech., 28(1), pp. 65–71. [CrossRef]
Terano, T., Masui, S., and Nagaya, K., 1992, “Experimental Study of Fuzzy Control for Bulldozer,” Proceedings of the IEEE Region 10 Conference, Tencon 92, Melbourne, Australia, Nov. 11–13, pp. 644–647.
Parker, C. A. C., Zhang, H., and Kube, C. R., 2003, “Blind Bulldozing: Multiple Robot Nest Construction,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, Las Vegas, NV, Oct., pp. 2010–2015.
Thangavelautham, J., El Samid, N. A., Grouchy, P., Earon, E., Fu, T., Nagrani, N., and D'Eleuterio, G. M. T., 2009, “Evolving Multirobot Excavation Controllers and Choice of Platforms Using an Artificial Neural Tissue Paradigm,” Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, pp. 258–265.
Bernold, L. E., 1993, “Motion and Path Control for Robotic Excavation,” J. Aerosp. Eng., 6(1), pp. 1–18. [CrossRef]
Bradley, D., and Seward, D., 1998, “The Development, Control, and Operation of an Autonomous Robotic Excavator,” J. Intell. Robotic Syst., 21(1), pp. 73–97. [CrossRef]
Stentz, A., Bares, J., Singh, A., and Rowe, P., 1999, “A Robotic Excavator for Autonomous Truck Loading,” Auton. Rob., 7(2), pp. 175–186. [CrossRef]
Ha, Q. P., Nguyen, Q. H., Rye, D. C., and Durrant-Whyte, H. F., 2000, “Impedance Control of a Hydraulically Actuated Robotic Excavator,” Autom. Constr., 9(5), pp. 421–435. [CrossRef]
Olsen, S. G., and Bone, G. M., 2012, “Development of a Hybrid Dynamic Model and Experimental Identification of Robotic Bulldozing,” ASME J. Dyn. Syst., Meas., Control, 135(2), p. 021015. [CrossRef]

Figures

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Fig. 1

Teleoperated bulldozer used for underground mining

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Fig. 2

Illustration of the state variables da, xb, zb, zc, φ, and ζ; and auxiliary variables ha, hb, and hc (note that Pb = [xb zb]T and Pc = [xc zc]T)

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Fig. 3

Illustration of the discrete operation modes (excluding modes 8 and 9)

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Fig. 4

Comparison of experimental results per pass

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Fig. 5

Example of an experimental result for pass 3 with Ctrl1

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