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Research Papers

A Multibody Dynamics Framework for Simulation of Rovers on Soft Terrain

[+] Author and Article Information
Ali Azimi

Mem. ASME
CM Labs Simulations, Inc.,
645 Wellington Street, Suite 301,
Montreal, QC H3C1T2, Canada
e-mail: ali.azimi@cm-labs.com

Daniel Holz

CM Labs Simulations, Inc.,
645 Wellington Street, Suite 301,
Montreal, QC H3C1T2, Canada
e-mail: daniel.holz@cm-labs.com

Jozsef Kövecses

Mem. ASME
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A0C3, Canada
Centre for Intelligent Machines,
McGill University,
Montreal, QC H3A2A7, Canada
e-mail: jozsef.kovecses@mcgill.ca

Jorge Angeles

Fellow ASME
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A0C3, Canada
Centre for Intelligent Machines,
McGill University,
Montreal, QC H3A2A7, Canada
e-mail: angeles@cim.mcgill.ca

Marek Teichmann

Mem. ASME
CM Labs Simulations, Inc.,
645 Wellington Street, Suite 301,
Montreal, QC H3C1T2, Canada
e-mail: marek@cm-labs.com

Vortex allows for changes of the stiffness and damping coefficients of any contact point at every simulation step.

Equation (24) needs special treatment: if βs → 90 deg, jy, which in turn means that the shear stress becomes equal to the shear strength, as the exponentially decaying term in Eq. (23) becomes zero. In our implementation in Vortex, βs is limited to a value slightly smaller than 90 deg to avoid any singularities.

The other cases, summation of angles equal to zero or 360 deg, do not have a physical meaning.

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received March 30, 2014; final manuscript received December 16, 2014; published online February 11, 2015. Assoc. Editor: Rudranarayan Mukherjee.

J. Comput. Nonlinear Dynam 10(3), 031004 (May 01, 2015) (12 pages) Paper No: CND-14-1084; doi: 10.1115/1.4029406 History: Received March 30, 2014; Revised December 16, 2014; Online February 11, 2015

A new framework is developed for efficient implementation of semi-empirical terramechanics models in multibody dynamics environments. In this approach, for every wheel in contact with soft soil, unilateral contact constraints are added for both the normal direction and the tangent plane. The forces associated with the latter, like traction and rolling resistance, are formulated in this approach as set-valued force laws, their properties being determined by deregularization of the terramechanics relations. As shown in the paper, this leads to the dynamics representation in the form of a linear complementarity problem (LCP). With this formulation, stable simulation of rovers is achieved even at relatively large time steps. In addition, a high-resolution height-field (HF) is employed to model terrain-surface deformation and changes in hardening of soil under the wheel. As a result, the multipass effect is captured in the presented approach. In addition, an extensive set of experiments was conducted using a version of the Juno rover (Juno II). The experimental results are analyzed and compared with the model developed in the paper.

Copyright © 2015 by ASME
Topics: Simulation , Soil , Wheels
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References

Figures

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Fig. 1

Normal stress distribution under a rigid wheel moving on an uncompacted soil as proposed by the WRI model

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Fig. 2

Schematic of wheel and soil contact in the planar case. (a) Original soil reaction representation and (b) equivalent soil reaction representation.

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Fig. 3

Forces acting on the soil wedge

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Fig. 4

Schematic illustration of determination of bulldozing force via integration over the submerged portion of the wheel sidewall

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Fig. 5

Illustration of contact points between a rolling cylindrical wheel and a planar terrain

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Fig. 6

Schematic of HF/wheel interaction and the approximating least-squares plane

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Fig. 7

Schematic of unloading/reloading model of Wong used to find normal stress distribution with multipass

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Fig. 9

An image from the simulation of Juno in Vortex

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Fig. 10

Comparing the drawbar pull obtained from experiments with the values obtained from simulation

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Fig. 11

Comparing the driving torque of the right side motor obtained from experiments with the values obtained from simulation

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Fig. 12

The HF terrain produced using the LIDAR scan data, with the initial rover location

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Fig. 13

The global position of the reflector, attached to the rover

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Fig. 14

The energy expenditure of the right side motor

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Fig. 15

The driving torque of the right side motor

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Fig. 16

The computation time involved in every simulation time step. The total computation time is divided into the time required for the collision detection algorithm and integration of the mathematical model.

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