Research Papers

Development of a Hybrid Dynamic Model and Experimental Identification of Robotic Bulldozing

[+] Author and Article Information
Scott G. Olsen

e-mail: olsensg@gmail.com

Gary M. Bone

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

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 15, 2011; final manuscript received September 25, 2012; published online December 21, 2012. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 135(2), 021015 (Dec 21, 2012) (10 pages) Paper No: DS-11-1391; doi: 10.1115/1.4023061 History: Received December 15, 2011; Revised September 25, 2012

The low-level modeling and control of mobile robots that interact forcibly with their environment, such as robotic excavation machinery, is a challenging problem that has not been adequately addressed in prior research. This paper investigates the low-level modeling of robotic bulldozing. The proposed model characterizes the three primary degrees-of-freedom (DOF) of the bulldozer, the blade position, the material accumulation on the blade, and the material distribution in the environment. It includes discrete operation modes contained within a hybrid dynamic model framework. The dynamics of the individual modes are represented by a set of linear and nonlinear differential equations. An instrumented scaled-down bulldozer and environment are developed to emulate the full scale operation. Model parameter estimation and validation are completed using experimental data from this system. The model is refined based on a global sensitivity analysis. The refined model is suitable for simulation and design of robotic bulldozing control strategies.

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Fig. 1

Teleoperated bulldozer used in underground mining

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Fig. 8

Example of a material profile scan

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Fig. 7

Photograph of the experimental robot and environment

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Fig. 6

Diagram of experimental robot and environment

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Fig. 5

Instrumented scaled-down robot bulldozer

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Fig. 4

Mode transition diagram

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Fig. 9

Example of the average material profile height along the robot path after zero, two, and four passes

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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. 10

Simulation of da, vb, and Φ dynamics with reduced and full sets of estimated parameters

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Fig. 11

Measured and predicted states for a 3-step ahead prediction horizon, rb and Γ from one pass of the validation data set




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