Hitoshi Kino

High-speed manipulation by using parallel wire-driven robots

A new type of a parallel wire-driven robot is proposed in order to reach ultra-high speed. The driving principle of parallel wire systems is described. Since wires can only pull and not push on an object, at least np1 wires are needed in order to move the object in a n-dimensional space. In this paper, taking account of the effect of such redundancy on actuation, the motion stability in wire length coordinates is analyzed by using a Lyapunov function. Using “Vector Closure”, it is proven that the hand position and orientation converge to the corresponding desired values and the internal force also converges to the desired one. Moreover, by making good use of non-linear elasticity of parallel wire driven robots, it is claimed that the internal force arising from redundant actuation can effectively reduce vibration when the high-speed robot stops at desired points. As a result, ultra-high speed with more than 40 g(g:gravitational acceleration) can be attained by using relatively small actuators.

Basic study of biarticular muscle's effect on muscular internal force control based on physiological hypotheses

In a musculoskeletal structure, the internal force among muscles plays an important role. Changing the internal force enables to control not only joint angles but also impedance, so that vertebrate animals can produce a motion according to a situation. Focusing on a musculoskeletal system with two links and six muscles, this paper investigate the effect of biarticular muscles when feedforward position control is inputted. This control gives the constant internal force balancing at desired posture as feedforward input, based on the EP hypothesis in physiology. From the result, we point out that the biarticular muscles can reduce the convergent time of the motion, and they also can stabilize the system.

Inverse Dynamics of Human Passive Motion Based on Iterative Learning Control

Estimation of joint torque is an important objective in the analyses of human motion. In particular, many applications seek to discern torque during a desired human motion, which is equivalent to solving the inverse dynamics. The computed torque method is a conventional means of calculating inverse dynamics. The obtained torque, however, invariably includes errors resulting from inexact inertial and viscoelastic parameters. This paper presents a method for solving the inverse dynamics of a human arm passively during tracking. To achieve precise human motion tracking, iterative learning control is used for motion generation. Some experiments that target a human arm are executed to validate the proposed method.

Numerical analysis of feedforward position control for non-pulley musculoskeletal system: a case study of muscular arrangements of a two-link planar system with six muscles

Abstract In a musculoskeletal system like a tendon-driven robot, redundant actuation is necessary because muscles (or mechanical parts such as tendons) can transmit tension only unidirectionally. This redundancy yields internal force among muscles, which has a particular field of potential energy. Using internal force as a feedforward input, a musculoskeletal system can achieve feedforward position control with no sensory feedback. This paper studies the feedforward position control coming from the redundancy for a non-pulley musculoskeletal system. Targeting a planar two-link system with six muscles as a case study, the motion convergence depending on the muscular arrangement is examined quasi-statically. The results point out that the convergence is extremely sensitive to the muscular arrangement, and adding small offsets for the muscular connected points can remarkably improve the positioning performance.

Sensory-motor control of a muscle redundant arm for reaching movements - convergence analysis and gravity compensation

In this paper, we study the sensory motor control mechanism in human reaching movements by considering the redundant muscle dynamics. We first formulate the kinematics and dynamics of a two-link arm model with six muscles, and introduce the nonlinear muscle dynamics based on the biological understanding. Secondly, we show the stability of the system by using intrinsic muscle characteristics and La Salles invariance theorem. From this result and the numerical simulations, we propose that the reaching movement can be regulated by the internal forces of the redundant muscles, in detail the muscles internal forces can be used to control the damping of the joints. In addition, human can compensate the gravity by using antigravity muscles. To realize this effect in the arm, we propose the gravity compensation method at the muscle input level from the viewpoint of robotics. We present the result of numerical simulation to verify the usefulness of this compensation method.

Task-space Feedback Control for A Two-link Arm Driven by Six Muscles with Variable Damping and Elastic Properties

It is well-known that a human musculo-skeletal body is redundant in terms of both kinematics and dynamics. The former means that the degree of freedom in joint space is larger than that in task space, and the latter means that a joint is driven by a number of muscles. All human skillful movements can be performed by using both redundancies. However, these redundancies induce the underlying ill-posedness problem that each joint angle and muscle’s output forces cannot be uniquely determined. These ill-posedness problems are known as “Bernstein’s problem” and are important to understand how human multi-joint movements are produced. In this study, we address the latter redundancy problem on how muscle’s output forces can be determined from the viewpoint of robotics. In this paper, we consider a reaching movement by means of a two-link planar arm with six muscles and show that both damping and elastic properties coming from nonlinear dynamics of the muscles play a crucial role. By using a simple task space feedback control input together with an additional term to control the internal force to regulate damping and elasticity in joint space, we show some simulation results which exhibit human-like quasi-straight line movement.

Principle of orthogonalization for completely restrained parallel wire driven robot

Parallel-wire driven robot utilizes light flexible wires instead of heavy rigid links, so it has some advantages such as high speed, heavy load and so on. Since wires can not push, but can pull an object, at least n + 1 wires are necessary in order to move an object in n-dimensional space for completely restrained parallel-wire driven system. A control scheme based on the wire length coordinates is very useful for the parallel-wire driven robots, since calculation of the inverse kinematics is generally easy as well as usual parallel-link robot. However the previous works could not prove the motion convergence of the system using more than n + 1 wires. In this paper, I investigate a principle of orthogonalization for completely restrained parallel-wire driven system. This principle clarifies the relation between wire tension and driving force-moment on the object. Using the principle of orthogonalization makes it possible to prove the motion convergence of the wire length feedback control utilizing more than n + 1 wires.

Set-point control of a musculoskeletal arm by the complementary combination of a feedforward and feedback manner

This paper proposes a novel set-point control method of a musculoskeletal system by combining a feedforward and feedback manner to complement each drawback each other. In our previous work, a feedforward positioning method of the musculoskeletal arm model was proposed which does not need any realtime sensory information. Its performance, however, depends on a muscular arrangement and an attitude of the arm, and thereby a large initial muscular internal force is necessary to make a good performance. On the other hand, it is well-known that a visual servoing is effective and versatile for the set-point control. However, there is a considerable time-delay due to a computational burden to acquire useful information from an image and an insufficient sampling period to capture each image when using a video frame rate camera. Thus in this paper, the feedforward and feedback signal are linearly combined into one in order to mutually complement each drawback. The combined control signal is newly designed and then numerical simulation results are shown to demonstrate the effectiveness and usefulness of the proposed method.

Iterative Learning Scheme for a Redundant Musculoskeletal Arm: Task Space Learning with Joint and Muscle Redundancies

This paper proposes an iterative learning control scheme in a task space for a musculoskeletal redundant planar arm model to accomplish a desired time dependent trajectory tracking task. In our previous work, we have proposed the iterative learning control scheme in a muscle length space for a two-link six-muscle planar arm model. This proposed method has been effective for performing a time dependent desired trajectory tracking task even under the existence of strong nonlinearity of muscles dynamics. However in the previous work, a muscle redundancy only treated, and a joint redundancy has not yet been considered. Also a solution of inverse kinematics from the task space to the joint angle space must be calculated in real-time. This paper considers both muscle and joint redundancies, and the task space iterative learning scheme is newly exploited. By introducing the task space controller, it is unnecessary to compute inverse kinematics from the task space to the joint space in real-time. Firstly, a three-joint nine-muscle redundant planar arm is modeled. Secondly, the task space iterative learning control signal is designed. Then finally, the effectiveness of our proposed controller is illustrated through some numerical simulation results even under the existence of both redundancies and the nonlinear muscle dynamics.

Torque estimation system for human leg in passive motion using parallel-wire driven mechanism and iterative learning control

Estimation of joint torque is one of the motion analyses for a humans. It has been applied to rehabilitation, virtual reality, sports training and so on. This paper presents the development of a torque estimation system for the human leg in passive motion. This system uses a parallel-wire driven mechanism using two wires and iterative learning control. From the viewpoint of motion range of the human leg, the non-singular area of the proposed system is analyzed in this paper. Finally the usefulness of our proposed method is demonstrated through some experimental results.