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Research Papers

Experimental Validation of the Tactile Exploration by a Manipulator With Joint Backlash

[+] Author and Article Information
Francesco Mazzini

 Director Field and Space Robotics Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139fmazzini@mit.edu

Steven Dubowsky

 Director Field and Space Robotics Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139dubowsky@mit.edu

A computationally faster approach involves the QR factorization of matrix A. But for few, sparse data the difference is negligible, on the order of the microsecond, and this approach is much simpler to implement.

This “normalized measure” is a simple way to evaluate all the parameters in a single number, by giving approximately the same weight to each of them. For each parameter, the difference between the real and the estimated value is divided by an appropriate normalizing constant (e.g., 360° for angles, the length of a manipulator’s arm for lengths, and so on). The measure is the square root of the sum of the square of such normalized differences.

J. Mechanisms Robotics 4(1), 011009 (Feb 03, 2012) (8 pages) doi:10.1115/1.4005334 History: Received February 23, 2011; Revised October 02, 2011; Published February 03, 2012; Online February 03, 2012

This research investigates using a manipulator to tactilely explore objects and environments when significant backlash affects its joint’s positions. A typical application is the exploration of rough environments, such as oil wells, where the harsh conditions dictate the use of tactile exploration. These conditions can result in large, unknown, and variable backlash in the manipulator’s transmissions, which strongly affects the measurement precision. Here, a method is developed to simultaneously map the unknown surface and identify the joint backlash. The robot probes the surface and uses its encoder readings to construct a partial map of the environment as a combination of geometric primitives. While the surface is built, the same data are also used to estimate backlash in the joints and to correct the surface measurements for backlash error. The effectiveness of the approach is demonstrated in simulation case studies and laboratory experiments.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Oil well branching structure, with detail showing the four-degree-of-freedom manipulator for tactile exploration

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Figure 2

The approach: simultaneous primitive estimation and backlash identification

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Figure 3

Backlash model, considering the effect of friction. When torque is low, the real position of the backlash is unknown and a uniform probability distribution is used.

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Figure 4

Weighted least squares according to the uncertainty due to small torques and joint friction

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Figure 5

Backlash generates error (and therefore uncertainty) in the distance function only when its effect on the tip position is normal to the surface

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Figure 6

The two environments chosen in the case studies: two barrels, explore from the outside (left) and L-shaped junction, explored from the inside (right)

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Figure 7

Progress of the backlash (top) and surface (bottom) estimates for the two barrels environment, when more touch points become available

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Figure 8

Progress of the backlash estimate for the L-shape while more points are touched. The detection of a primitive is shown by its name on the graph.

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Figure 9

Planar manipulator used in the line probing and detail of one of the motor-gearbox units

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Figure 10

Probed points and best fit lines with and without backlash compensation, relative to the real line

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Figure 11

Experimental prototype for oil well exploration. Left: details of the arm; right: robot inside the modeled oil well junction.

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Figure 12

Exploration and backlash estimation of the three-planes environment by the prototype manipulator. Left: robot and environment. Center: the reconstructed surface, with the touched points. The intensity of the points indicates the amount of torque in the first joint, correlated to the identifiability. Right: progress of the backlash estimate while more points are touched.

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