Limits...
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.


UCM and ORT components for both the simulation and measurement experiments. The upper left (A,C) and right (B,D) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the UCM and ORT components 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. (E) The mean value for the UCM components across the movement duration for all experiments. The n. s. indicates not significant (one-sample t-test). (F) The mean value for the ORT component.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4940408&req=5

Figure 7: UCM and ORT components for both the simulation and measurement experiments. The upper left (A,C) and right (B,D) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the UCM and ORT components 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. (E) The mean value for the UCM components across the movement duration for all experiments. The n. s. indicates not significant (one-sample t-test). (F) The mean value for the ORT component.

Mentions: Figures 7A–D show the waveforms of the UCM and ORT components. The upper and middle figures show results for the UCM and ORT components, respectively. The red and blue solid lines indicate the results of simulation and measurement experiments. The blue area denotes the standard deviation across all subjects. In both the simulation and measurement experiments, the mean waveforms of the UCM components were larger than those of the ORT components throughout the duration of movement, which indicates that the variance of joint angles was more varied across the UCM. Moreover, the UCM and ORT components gradually increased from the movement initiation to the end and our proposed method could generate same tendency. Figures 7E,F show the mean values for the UCM and ORT components for all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a similar mean value for the UCM and ORT components from the measurement experiments [the UCM component in the far position task: t(7) = −0.33, P = 0.75; in the near position task: t(7) = 0.54, P = 0.60; the ORT component in the far position task: t(7) = 0.28, P = 0.79; in the near position task: t(7) = 0.73, P = 0.49].


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

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

UCM and ORT components for both the simulation and measurement experiments. The upper left (A,C) and right (B,D) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the UCM and ORT components 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. (E) The mean value for the UCM components across the movement duration for all experiments. The n. s. indicates not significant (one-sample t-test). (F) The mean value for the ORT component.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: UCM and ORT components for both the simulation and measurement experiments. The upper left (A,C) and right (B,D) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the UCM and ORT components 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. (E) The mean value for the UCM components across the movement duration for all experiments. The n. s. indicates not significant (one-sample t-test). (F) The mean value for the ORT component.
Mentions: Figures 7A–D show the waveforms of the UCM and ORT components. The upper and middle figures show results for the UCM and ORT components, respectively. The red and blue solid lines indicate the results of simulation and measurement experiments. The blue area denotes the standard deviation across all subjects. In both the simulation and measurement experiments, the mean waveforms of the UCM components were larger than those of the ORT components throughout the duration of movement, which indicates that the variance of joint angles was more varied across the UCM. Moreover, the UCM and ORT components gradually increased from the movement initiation to the end and our proposed method could generate same tendency. Figures 7E,F show the mean values for the UCM and ORT components for all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a similar mean value for the UCM and ORT components from the measurement experiments [the UCM component in the far position task: t(7) = −0.33, P = 0.75; in the near position task: t(7) = 0.54, P = 0.60; the ORT component in the far position task: t(7) = 0.28, P = 0.79; in the near position task: t(7) = 0.73, P = 0.49].

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.