Shared Control in Haptic Systems for Performance Enhancement and Training

[+] Author and Article Information
Marcia K. O’Malley

Mechanical Engineering and Materials Science, Rice University, Houston, TX 77005omalleym@rice.edu

Abhishek Gupta

Mechanical Engineering and Materials Science, Rice University, Houston, TX 77005abhi@rice.edu

Matthew Gen

Mechanical Engineering and Materials Science, Rice University, Houston, TX 77005mgen@rice.edu

Yanfang Li

Mechanical Engineering and Materials Science, Rice University, Houston, TX 77005yvonneli@rice.edu

J. Dyn. Sys., Meas., Control 128(1), 75-85 (Nov 30, 2005) (11 pages) doi:10.1115/1.2168160 History: Received April 02, 2005; Revised November 30, 2005

This paper presents a shared-control interaction paradigm for haptic interface systems, with experimental data from two user studies. Shared control, evolved from its initial telerobotics applications, is adapted as a form of haptic assistance in that the haptic device contributes to execution of a dynamic manual target-hitting task via force commands from an automatic controller. Compared to haptic virtual environments, which merely display the physics of the virtual system, or to passive methods of haptic assistance for performance enhancement based on virtual fixtures, the shared-control approach offers a method for actively demonstrating desired motions during virtual environment interactions. The paper presents a thorough review of the literature related to haptic assistance. In addition, two experiments were conducted to independently verify the efficacy of the shared-control approach for performance enhancement and improved training effectiveness of the task. In the first experiment, shared control is found to be as effective as virtual fixtures for performance enhancement, with both methods resulting in significantly better performance in terms of time between target hits for the manual target-hitting task than sessions where subjects feel only the forces arising from the mass-spring-damper system dynamics. Since shared control is more general than virtual fixtures, this approach may be extremely beneficial for performance enhancement in virtual environments. In terms of training enhancement, shared control and virtual fixtures were no better than practice in an unassisted mode. For manual control tasks, such as the one described in this paper, shared control is beneficial for performance enhancement, but may not be viable for enhancing training effectiveness.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Subject seated at IE2000, viewing the target-hitting task

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Figure 2

Graphical display of tapping experiment. Subjects control location of m1 in order to cause m2 to hit the desired target. Targets appear in pairs (NF: negative slope, far; NN: negative slope, near; PF: positive slope, far; PN: positive slope, near). Inset shows virtual underactuated system. The user controls the system by applying forces to mass m1 through a joystick based interface.

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Figure 3

Block diagram of shared-controller architecture. The position and velocity of the joystick are mapped directly to that of m1. The shared controller computes the force Fs (Eqs. 10,11) that should act on m1 in order to follow the reference dynamics for m2, as defined by Eqs. 8,9. The force Fs is reflected back through the haptic device in addition to the forces arising from system dynamics, Fk (Eqs. 5,6).

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Figure 4

Force profiles for interactions with a target pair aligned with the x-axis with (a) no assistance, (b) virtual fixtures for assistance, and (c) shared control for assistance. In (a), the total commanded force felt by the user, Fx and Fy, is due entirely to the forces that arise from the dynamics of the virtual spring-mass system, and due to the target alignment, the predominant forces are in the x direction. In (b), note the existence of significant forces in the y direction despite target-pair alignment equivalent to case (a). The magnitude of Fy is due to contributions from the spring-mass system forces (Fky) and the virtual fixture feedback (Fpy). Since the target pair for this trial is aligned in x, motion in the y direction, leads to force feedback from the virtual fixture. This is the primary component of Fy, the total y-axis force displayed with the haptic device. In case (c), Fky is the force due to excitation of the spring-mass-damper system, while Fsy is the force due to the shared controller. Fsy is calculated such that the y-axis motion of m2 is suppressed. Similar to (b) Fy is significant, but the forces are a function of all of the system’s state variables, not just the position of m1.

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Figure 5

Traces show representative paths of m1 (black) and m2 (gray) in the workspace in each mode of interaction. The no assistance mode (a) constrains neither the motion of m1 nor the motion of m2. In the virtual fixture assistance mode (b), the motion of m1 is constrained to lie along a straight-line path between the target pair. Subjects experience a resistive force proportional to the distance deviated from this line. The motion of m2 remains unconstrained. In the shared-control assistance mode (c), the motion of m2 is controlled such that any motion orthogonal to the path between the targets is suppressed, therefore, making it relatively easy for subjects to maintain motion of m2 along the target axis. The motion of m1 in this mode is not directly constrained.

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Figure 6

Experiment 1: Average time between target hits for performance assessment experiment. Shared control and assistance using virtual fixtures both provide significant and similar improvement in user performance as compared to the no-assistance case. A significant interaction was found between distance and assistance modes. The results for positive and negative sloped target pairs are combined in this figure.

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Figure 7

Experiment 2: Baseline performance measures across training sessions. Subjects’ performance in an unassisted mode was measured before and after a training session in one of three assistance modes ((a) no assistance, (b) virtual fixture, or (c) shared control). Results for near and far targets are combined in this presentation. The lower bound on performance is approximately 700ms, limited by the natural frequency of the virtual mass-spring-damper system.

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Figure 8

Experiment 2: Regression in task performance between training sessions. Y-axis value is the raw B2i score minus the raw B1i+1 score, averaged across subjects in each training group, where i represents the training session number. In other words, the performance at the end of a given training session is compared to the performance at the start of the following training session, with both performance measures recorded without any form of haptic assistance. Negative values indicate that the average time between hits has decreased from one session to the next, implying that the subjects’ performance has improved since the last session. Positive values indicate that the average time between hits has increased (i.e., the performance has degraded since the last session). Only the shared-control and virtual fixture groups experience a performance improvement between sessions. The no-assistance group experiences degradation in performance between each training session, such that the task must be relearned before additional improvements in performance can be realized.



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