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. 2014 Jan 21;14(1):1787-804.
doi: 10.3390/s140101787.

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors' feedback

Carlos A Jara  1 , Jorge Pomares  2 , Francisco A Candelas  3 , Fernando Torres  4
Affiliations

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors' feedback

Carlos A Jara et al. Sensors (Basel). .

Abstract

Tactile sensors play an important role in robotics manipulation to perform dexterous and complex tasks. This paper presents a novel control framework to perform dexterous manipulation with multi-fingered robotic hands using feedback data from tactile and visual sensors. This control framework permits the definition of new visual controllers which allow the path tracking of the object motion taking into account both the dynamics model of the robot hand and the grasping force of the fingertips under a hybrid control scheme. In addition, the proposed general method employs optimal control to obtain the desired behaviour in the joint space of the fingers based on an indicated cost function which determines how the control effort is distributed over the joints of the robotic hand. Finally, authors show experimental verifications on a real robotic manipulation system for some of the controllers derived from the control framework.

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Figures

Figure 1.
Figure 1.
(a) Allegro hand with the tactile sensors installed in the fingertips' surface. (b) Allegro hand grasping the object to be manipulated. (c) Different 3D representation of the pressure measurements registered by the arrays of tactile sensors.
Figure 2.
Figure 2.
Experimental setup for dexterous manipulation (robotic hand, manipulated object, eye-to-hand camera and reference systems).
Figure 3.
Figure 3.
Image trajectories obtained during the first set of experiments. (a) Desired image trajectory. (b) W = M−2. (c) W = DM−2. (d) W = M−1.
Figure 4.
Figure 4.
Torques obtained during the first set of experiments. (a) First finger. (b) Second finger. (c) Third finger. For each figure the torques obtained by the controllers indicated in the legend of Figure 3 are represented.
Figure 5.
Figure 5.
Mean total contact force during the first set of experiments. (a) W = M−2. (b) W = DM−2. (c) W = M−1.
Figure 6.
Figure 6.
Distribution of the pressure measurements registered by the arrays of tactile sensors of the three fingers in an intermediate iteration during the manipulation task (first task).
Figure 7.
Figure 7.
Image trajectories obtained during the second set of experiments. (a) Desired image trajectory. (b) W = M−2. (c) W = DM−2. (d) W = M−1.
Figure 8.
Figure 8.
3D trajectories of the manipulated object obtained during the second set of experiments.
Figure 9.
Figure 9.
Mean total contact force during the second set of experiments. (a) W = M−2. (b) W = DM−2. (c) W = M−1.
Figure 10.
Figure 10.
Distribution of the pressure measurements registered by the arrays of tactile sensors of the three fingers in an intermediate iteration during the manipulation task (second task).
See this image and copyright information in PMC

References

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    1. Han L., Trinkle J. Dexterous Manipulation by Rolling and Finger Gaiting. Proceedings of IEEE International Conference on Robotics and Automatio; Leuven, Belgium. 16–20 May 1998; pp. 730–735.
    1. Yoshikawa T. Multifingered robot hands: Control for grasping and manipulation. Ann. Rev. Control. 2010;34:199–208.
    1. Trinkle J., Hunter J. A Framework for Planning Dexterous Manipulation. Proceedings of IEEE International Conference on Robotics and Automation; Sacramento, CA, USA. 9–11 April 1991; pp. 1245–1251.

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