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TECHNICAL PAPERS

# Identification of Contact Dynamics Model Parameters From Constrained Robotic Operations

[+] Author and Article Information
M. Weber

IKS GmbH, Germany

K. Patel

Department of Mechanical Engineering, McGill University, Montreal, QC H3A 2K6, Canada

O. Ma

Department of Mechanical Engineering, New Mexico State University, Las Cruces, NM 88003

I. Sharf

Department of Mechanical Engineering, McGill University, Montreal, QC H3A 2K6, Canadainna.sharf@mcgill.ca

Note: $ti$ is defined here to be opposite to the relative tangential velocity.

J. Dyn. Sys., Meas., Control 128(2), 307-318 (May 10, 2005) (12 pages) doi:10.1115/1.2192839 History: Received July 10, 2003; Revised May 10, 2005

## Abstract

With the fast advances in computing technology, contact dynamics simulations are playing a more important role in the design, verification, and operation support of space systems. The validity of computer simulation depends not only on the underlying mathematical models but also on the model parameters. This paper describes a novel strategy of identifying contact dynamics parameters based on the sensor data collected from a robot performing contact tasks. Unlike existing identification algorithms, this methodology is applicable to complex contact geometries where contact between mating objects occurs at multiple surface areas in a time-variant fashion. At the same time, the procedure requires only measurements of end-effector forces/moments and the kinematics information for the end-effector and the environment. Similarly to other methods, the solution is formulated as a linear identification problem, which can be solved with standard numerical techniques for overdetermined systems. Efficacy, precision, and sensitivity of the identification methodology are investigated in simulation with two examples: A cube sliding in a wedge and a payload/fixture combination modeled after a real space-manipulator task.

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## Figures

Figure 1

Typical single-point contact

Figure 2

Typical multiple-point contact

Figure 3

Schematic illustration of use of mindist

Figure 4

Cube in a wedge model in MSC ADAMS

Figure 5

Snapshots of ADAMS simulation of cube sliding in a wedge at t=0.0, 0.065, and 0.13s (top to bottom)

Figure 6

Displacement components of center of cube (cube in a wedge example)

Figure 7

Identified contact parameters (cube in a wedge example)

Figure 8

EIA battery

Figure 9

Schematic illustration of validation procedure for the algorithm

Figure 10

(a) Total number of contact points, Z-coordinate and norm of total contact force plot for IEA battery drop test case. B=0kg∕s. (b) Identified contact parameters (IEA battery drop test case, B=0kg∕s).

Figure 11

(a) Total number of contact points, Z-coordinate and norm of total contact force plot for IEA battery drop test case. B=250kg∕s. (b) Identified contact parameters (IEA battery drop test case, B=250kg∕s).

Figure 12

Identified contact parameters by excluding the sticking points (IEA battery drop test case, νdeadband=0.008m∕s and R¯s=0%)

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