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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.


Sum of the squared torques for all joints (shoulder, elbow, and wrist) for both the simulation and measurement experiments. The upper left (A) and right (B) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the sum of the squared torque 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 the sum of the squared torque for all experiments. The n. s. indicates not significant (one-sample t-test). (D) The mean value of the sum of the squared torque change for all experiments.
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Figure 6: Sum of the squared torques for all joints (shoulder, elbow, and wrist) for both the simulation and measurement experiments. The upper left (A) and right (B) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the sum of the squared torque 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 the sum of the squared torque for all experiments. The n. s. indicates not significant (one-sample t-test). (D) The mean value of the sum of the squared torque change for all experiments.

Mentions: Figures 6A,B show the profiles of the sum of the squared torques for all joints. The red and blue solid lines indicate the results of the simulation and measurement experiments. The blue area denotes the standard deviation across all subjects. The sum of the squared torque was also bell-shaped while the profiles for the simulation and measurement experiments were similar. Figure 6C shows the peak of the sum of the squared joint torque for all experiments. A one-sample t-test between simulation and measurement results indicated that our proposed method could generate a similar peak to the measurement experiments [the far position task: t(7) = 0.84, P = 0.43; the near position task: t(7) = 0.38, P = 0.71]. 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) = 1.37, P = 0.21]. However, for the measurement experiments the mean peak of the tangential hand velocity in the far position task [0.200 (Nm)2] tended to be smaller than that for the near position task [0.246 (Nm)2]. Our proposed method also showed the same tendency [far: 0.174 (Nm)2; near: 0.239 (Nm)2]. Figure 6D shows the mean sum of the squared torque change for all experiments. Figure 6D shows the sum of the squared joint torque change of all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a peak value similar to the measurement experiments [the far position task: t(7) = 1.30, P = 0.23; the near position task: t(7) = 0.80, P = 0.45].


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

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

Sum of the squared torques for all joints (shoulder, elbow, and wrist) for both the simulation and measurement experiments. The upper left (A) and right (B) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the sum of the squared torque 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 the sum of the squared torque for all experiments. The n. s. indicates not significant (one-sample t-test). (D) The mean value of the sum of the squared torque change for all experiments.
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Show All Figures
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Figure 6: Sum of the squared torques for all joints (shoulder, elbow, and wrist) for both the simulation and measurement experiments. The upper left (A) and right (B) graphs correspond to the far position and near position tasks. The red and blue lines indicate the mean value profiles of the sum of the squared torque 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 the sum of the squared torque for all experiments. The n. s. indicates not significant (one-sample t-test). (D) The mean value of the sum of the squared torque change for all experiments.
Mentions: Figures 6A,B show the profiles of the sum of the squared torques for all joints. The red and blue solid lines indicate the results of the simulation and measurement experiments. The blue area denotes the standard deviation across all subjects. The sum of the squared torque was also bell-shaped while the profiles for the simulation and measurement experiments were similar. Figure 6C shows the peak of the sum of the squared joint torque for all experiments. A one-sample t-test between simulation and measurement results indicated that our proposed method could generate a similar peak to the measurement experiments [the far position task: t(7) = 0.84, P = 0.43; the near position task: t(7) = 0.38, P = 0.71]. 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) = 1.37, P = 0.21]. However, for the measurement experiments the mean peak of the tangential hand velocity in the far position task [0.200 (Nm)2] tended to be smaller than that for the near position task [0.246 (Nm)2]. Our proposed method also showed the same tendency [far: 0.174 (Nm)2; near: 0.239 (Nm)2]. Figure 6D shows the mean sum of the squared torque change for all experiments. Figure 6D shows the sum of the squared joint torque change of all experiments. A one-sample t-test between the simulation and measurement results indicated that our proposed method could generate a peak value similar to the measurement experiments [the far position task: t(7) = 1.30, P = 0.23; the near position task: t(7) = 0.80, P = 0.45].

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.