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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.


Tangential hand velocity for both the simulation and measurement experiments. The upper left (A) and right (B) graphs denote the far position and near position tasks. The red and blue lines indicate the mean value profiles for the tangential hand velocity across all trials for the simulation experiments, and across all subjects for the measurement experiments. The blue area denotes the standard deviation across all subjects. (C) Peak of tangential hand velocity of 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 5: Tangential hand velocity for both the simulation and measurement experiments. The upper left (A) and right (B) graphs denote the far position and near position tasks. The red and blue lines indicate the mean value profiles for the tangential hand velocity across all trials for the simulation experiments, and across all subjects for the measurement experiments. The blue area denotes the standard deviation across all subjects. (C) Peak of tangential hand velocity of 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: Figures 5A,B show the profiles of tangential velocity of the hand. The red and blue solid lines indicate the results of simulation and measurement experiments. The blue area denotes standard deviation across all subjects. The target hand trajectory in the one-dimensional target-tracking task had a bell-shaped and smooth velocity profile with a peak at the middle of the movement duration (2.5 s). In both the simulation and measurement experiments, the profiles of the tangential velocity of the hand were also bell-shaped. Figure 5C shows the peak of the tangential hand velocity for all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a similar peak compared to the measurement experiments [the far position task: t(7) = 1.93, P = 0.09; the near position task: t(7) = 2.27, P = 0.06]. A paired t-test demonstrated that the measured peak of the tangential hand velocities was not significantly different between the far and near position tasks [t(7) = 1.10, P = 0.31]. However, in the measurement experiments the mean peak of the tangential hand velocity in the far position task (0.135 m/s) tended to be larger than that of the near position task (0.128 m/s). Our proposed method also showed the same tendency (the far position task: 0.124 m/s; the near position task: 0.119 m/s).


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

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

Tangential hand velocity for both the simulation and measurement experiments. The upper left (A) and right (B) graphs denote the far position and near position tasks. The red and blue lines indicate the mean value profiles for the tangential hand velocity across all trials for the simulation experiments, and across all subjects for the measurement experiments. The blue area denotes the standard deviation across all subjects. (C) Peak of tangential hand velocity of 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 5: Tangential hand velocity for both the simulation and measurement experiments. The upper left (A) and right (B) graphs denote the far position and near position tasks. The red and blue lines indicate the mean value profiles for the tangential hand velocity across all trials for the simulation experiments, and across all subjects for the measurement experiments. The blue area denotes the standard deviation across all subjects. (C) Peak of tangential hand velocity of 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: Figures 5A,B show the profiles of tangential velocity of the hand. The red and blue solid lines indicate the results of simulation and measurement experiments. The blue area denotes standard deviation across all subjects. The target hand trajectory in the one-dimensional target-tracking task had a bell-shaped and smooth velocity profile with a peak at the middle of the movement duration (2.5 s). In both the simulation and measurement experiments, the profiles of the tangential velocity of the hand were also bell-shaped. Figure 5C shows the peak of the tangential hand velocity for all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a similar peak compared to the measurement experiments [the far position task: t(7) = 1.93, P = 0.09; the near position task: t(7) = 2.27, P = 0.06]. A paired t-test demonstrated that the measured peak of the tangential hand velocities was not significantly different between the far and near position tasks [t(7) = 1.10, P = 0.31]. However, in the measurement experiments the mean peak of the tangential hand velocity in the far position task (0.135 m/s) tended to be larger than that of the near position task (0.128 m/s). Our proposed method also showed the same tendency (the far position task: 0.124 m/s; the near position task: 0.119 m/s).

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.