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Uncontrolled Manifold Reference Feedback Control of Multi-Joint Robot Arms.

Togo S, Kagawa T, Uno Y - Front Comput Neurosci (2016)

Bottom Line: The target UCM is a subspace of joint angles whose variability does not affect the hand position.As a result, the UCM reference feedback control could quantitatively reproduce the difference of the mean value for the end hand position between the initial postures, the peaks of the bell-shape tangential hand velocity, the sum of the squared torque, the mean value for the torque change, the variance components, and the index of synergy as well as the human subjects.We concluded that UCM reference feedback control can reproduce human-like joint coordination.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute InternationalKyoto, Japan; Japan Society for the Promotion of ScienceTokyo, Japan.

ABSTRACT
The brain must coordinate with redundant bodies to perform motion tasks. The aim of the present study is to propose a novel control model that predicts the characteristics of human joint coordination at a behavioral level. To evaluate the joint coordination, an uncontrolled manifold (UCM) analysis that focuses on the trial-to-trial variance of joints has been proposed. The UCM is a nonlinear manifold associated with redundant kinematics. In this study, we directly applied the notion of the UCM to our proposed control model called the "UCM reference feedback control." To simplify the problem, the present study considered how the redundant joints were controlled to regulate a given target hand position. We considered a conventional method that pre-determined a unique target joint trajectory by inverse kinematics or any other optimization method. In contrast, our proposed control method generates a UCM as a control target at each time step. The target UCM is a subspace of joint angles whose variability does not affect the hand position. The joint combination in the target UCM is then selected so as to minimize the cost function, which consisted of the joint torque and torque change. To examine whether the proposed method could reproduce human-like joint coordination, we conducted simulation and measurement experiments. In the simulation experiments, a three-link arm with a shoulder, elbow, and wrist regulates a one-dimensional target of a hand through proposed method. In the measurement experiments, subjects performed a one-dimensional target-tracking task. The kinematics, dynamics, and joint coordination were quantitatively compared with the simulation data of the proposed method. As a result, the UCM reference feedback control could quantitatively reproduce the difference of the mean value for the end hand position between the initial postures, the peaks of the bell-shape tangential hand velocity, the sum of the squared torque, the mean value for the torque change, the variance components, and the index of synergy as well as the human subjects. We concluded that UCM reference feedback control can reproduce human-like joint coordination. The inference for motor control of the human central nervous system based on the proposed method was discussed.

No MeSH data available.


Related in: MedlinePlus

Initial and end postures of the arm in the simulation and measurement experiments. The upper left (A,C) and upper right (B,D) graphs correspond to the far position task and near position task. The horizontal axis and vertical axis denote the medial–lateral direction and anterior–posterior directions. The blue lines denote the initial posture and the red lines denote the end postures. The origin is the center of gyration of the shoulder. (E) The mean value of the end hand position in the Y-direction for all experiments. The n. s. indicates not significant (one-sample t-test), and the asterisk denotes a significant difference (paired t-test, P < 0.05).
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Figure 3: Initial and end postures of the arm in the simulation and measurement experiments. The upper left (A,C) and upper right (B,D) graphs correspond to the far position task and near position task. The horizontal axis and vertical axis denote the medial–lateral direction and anterior–posterior directions. The blue lines denote the initial posture and the red lines denote the end postures. The origin is the center of gyration of the shoulder. (E) The mean value of the end hand position in the Y-direction for all experiments. The n. s. indicates not significant (one-sample t-test), and the asterisk denotes a significant difference (paired t-test, P < 0.05).

Mentions: Figure 3 shows the start and end arm postures. The upper (Figures 3A,B) and middle (Figures 3C,D) figures indicate the results of the simulation and the measurement experiments. In both experiments, hand positions at the movement end were more varied in the anterior–posterior direction (Y-direction), and the variance of joint angles did not affect task achievement. Hand positions in the near position task were nearer the trunk at the movement end than those in the far position task. Figure 3E shows the mean hand position at the movement end in the Y-direction for all experiments. Our proposed method generated quantitatively similar hand positions to those of all subjects. Statistically, a one-sample t-test between the simulation and measurement results showed no significant difference [the far position task: t(7) = −0.45, P = 0.67; the near position task: t(7) = −1.06, P = 0.33]. Moreover, a paired t-test demonstrated that the measured hand positions in the near position task were significantly nearer than those in the far position task [t(7) = 4.50, P = 0.0028 < 0.05], and the proposed method could generate same tendency (the far position task: 0.38 m; the near position task: 0.29 m).


Uncontrolled Manifold Reference Feedback Control of Multi-Joint Robot Arms.

Togo S, Kagawa T, Uno Y - Front Comput Neurosci (2016)

Initial and end postures of the arm in the simulation and measurement experiments. The upper left (A,C) and upper right (B,D) graphs correspond to the far position task and near position task. The horizontal axis and vertical axis denote the medial–lateral direction and anterior–posterior directions. The blue lines denote the initial posture and the red lines denote the end postures. The origin is the center of gyration of the shoulder. (E) The mean value of the end hand position in the Y-direction for all experiments. The n. s. indicates not significant (one-sample t-test), and the asterisk denotes a significant difference (paired t-test, P < 0.05).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4940408&req=5

Figure 3: Initial and end postures of the arm in the simulation and measurement experiments. The upper left (A,C) and upper right (B,D) graphs correspond to the far position task and near position task. The horizontal axis and vertical axis denote the medial–lateral direction and anterior–posterior directions. The blue lines denote the initial posture and the red lines denote the end postures. The origin is the center of gyration of the shoulder. (E) The mean value of the end hand position in the Y-direction for all experiments. The n. s. indicates not significant (one-sample t-test), and the asterisk denotes a significant difference (paired t-test, P < 0.05).
Mentions: Figure 3 shows the start and end arm postures. The upper (Figures 3A,B) and middle (Figures 3C,D) figures indicate the results of the simulation and the measurement experiments. In both experiments, hand positions at the movement end were more varied in the anterior–posterior direction (Y-direction), and the variance of joint angles did not affect task achievement. Hand positions in the near position task were nearer the trunk at the movement end than those in the far position task. Figure 3E shows the mean hand position at the movement end in the Y-direction for all experiments. Our proposed method generated quantitatively similar hand positions to those of all subjects. Statistically, a one-sample t-test between the simulation and measurement results showed no significant difference [the far position task: t(7) = −0.45, P = 0.67; the near position task: t(7) = −1.06, P = 0.33]. Moreover, a paired t-test demonstrated that the measured hand positions in the near position task were significantly nearer than those in the far position task [t(7) = 4.50, P = 0.0028 < 0.05], and the proposed method could generate same tendency (the far position task: 0.38 m; the near position task: 0.29 m).

Bottom Line: The target UCM is a subspace of joint angles whose variability does not affect the hand position.As a result, the UCM reference feedback control could quantitatively reproduce the difference of the mean value for the end hand position between the initial postures, the peaks of the bell-shape tangential hand velocity, the sum of the squared torque, the mean value for the torque change, the variance components, and the index of synergy as well as the human subjects.We concluded that UCM reference feedback control can reproduce human-like joint coordination.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute InternationalKyoto, Japan; Japan Society for the Promotion of ScienceTokyo, Japan.

ABSTRACT
The brain must coordinate with redundant bodies to perform motion tasks. The aim of the present study is to propose a novel control model that predicts the characteristics of human joint coordination at a behavioral level. To evaluate the joint coordination, an uncontrolled manifold (UCM) analysis that focuses on the trial-to-trial variance of joints has been proposed. The UCM is a nonlinear manifold associated with redundant kinematics. In this study, we directly applied the notion of the UCM to our proposed control model called the "UCM reference feedback control." To simplify the problem, the present study considered how the redundant joints were controlled to regulate a given target hand position. We considered a conventional method that pre-determined a unique target joint trajectory by inverse kinematics or any other optimization method. In contrast, our proposed control method generates a UCM as a control target at each time step. The target UCM is a subspace of joint angles whose variability does not affect the hand position. The joint combination in the target UCM is then selected so as to minimize the cost function, which consisted of the joint torque and torque change. To examine whether the proposed method could reproduce human-like joint coordination, we conducted simulation and measurement experiments. In the simulation experiments, a three-link arm with a shoulder, elbow, and wrist regulates a one-dimensional target of a hand through proposed method. In the measurement experiments, subjects performed a one-dimensional target-tracking task. The kinematics, dynamics, and joint coordination were quantitatively compared with the simulation data of the proposed method. As a result, the UCM reference feedback control could quantitatively reproduce the difference of the mean value for the end hand position between the initial postures, the peaks of the bell-shape tangential hand velocity, the sum of the squared torque, the mean value for the torque change, the variance components, and the index of synergy as well as the human subjects. We concluded that UCM reference feedback control can reproduce human-like joint coordination. The inference for motor control of the human central nervous system based on the proposed method was discussed.

No MeSH data available.


Related in: MedlinePlus