This study focuses on replicating the muscu-loskeletal system of human arms for mimicking its movement. Muscle redundancy is critical for regulating the mechanical impedance of arms and legs. However, when implementing muscle redundancy on robots, making an ill-posed problem that cannot determine the muscle forces uniquely. In this paper, first, a method for controlling end-point stiffness in the muscle space for the joint and muscle redundant system is described. Next, the muscle model imitating the nonlinear viscosity characteristic of human muscles is introduced. Then, a method to control the joint viscosity by adjusting the internal forces of muscles adequately without affecting the stiffness control directly is proposed. Finally, numerical simulations are performed to investigate the effectiveness of the proposed method.

In this paper, our previously proposed set-point control method for a musculoskeletal system is improved to reduce required muscular forces and to avoid a saturation of muscular forces during movement. The previous method is robust against a considerable time-delay in sensory information, but it still requires large muscular forces to accomplish a desired position, and the maximum exertable muscular force has not yet been taken into consideration. To cope with these two issues, two variable parameters are newly introduced. One is for changing the combination ratio of feed-forward and feedback controllers to reduce necessary muscular forces. The other is for avoiding the saturation of muscular forces during movement The effectiveness of the proposed controller is demonstrated through several numerical simulation results.

This paper considers experimental investigation of an antagonistically actuated robot manipulator. The focus of the investigation is how biarticular actuation contributes to control of the manipulator. Based on the feature that antagonistically robot manipulators can be controlled by feedforward control with constant inputs, this paper treats the case of PTP (point-to-point) control of robot postures. Due to expectation of difficulty for analytical approach, this paper takes experimental methodology of acquiring mappings between sensory and motor spaces by ANNs (artificial neural networks). Based on the idea that contribution of biarticular actuation may be influenced by choice of a sensory space, this paper considers not only joint angles of a manipulator but also Cartesian coordinate and angles in binocular visual space at the hand of the manipulator as sensory spaces. For each of them, the mappings obtained from trained ANNs are compared for the cases with and without biarticular actuation, based on interpolation performance of the ANNs examined through PTP control of the manipulator. The results of the comparison shows that choice of a sensory space is less effective to the interpolation performance in the case of biarticular actuation.

The human body possesses a musculoskeletal structure in which muscles exist around the bones and joints. The musculoskeletal tendon-driven robot utilizes this structure. This robotic system uses sets of mechanical tendons, such as wire-cables and actuators instead of the vital muscles. The redundant actuation is necessary for the system when it does not actively use any external force nor a tensioner because the mechanical tendon can transmit only a tensile force. This structural characteristic enables feedforward motion-generation that does not need any sensory feedback. However, the convergent posture strongly depends on the tendon-arrangement. Targeting the tendon-driven manipulator, which has two links and six tendons, this paper expands the mathematical conditions for the convergence into the geometric conditions of tendon-arrangement. Based on the geometric conditions, a design method of the tendon-arrangement is discussed.

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.

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.

This paper presents a proposal of an iterative learning control method for a musculoskeletal arm to acquire adequate internal force to realize human-like natural movements. Additionally, a dynamic damping ellipsoid at the end-point is introduced to evaluate internal forces obtained through the iterative learning. In our previous works, we have presented that a human-like smooth reaching movement using a musculoskeletal redundant arm model can be obtained by introducing a nonlinear muscle model and “the Virtual spring-damper hypothesis”. However, the internal forces have been determined heuristically, so far. In this paper, an iterative learning control method is used for acquisition of an adequate dynamic damping ellipsoid according to a given task, in order to determine internal forces more systematically. It is presented that the learning control scheme can perform effectively to realize given desired tasks, even under the existence of strong nonlinear characteristics of the muscles. After acquiring a given task, the dynamic damping ellipsoid is introduced to evaluate the relation between a damping effect generated by the acquired internal forces and a trajectory of the end-point. Some numerical simulations are performed and the usefulness of the learning control strategy, despite strong nonlinearity of the muscles, is demonstrated through these results.

A cylindrical elastic elements (CEE) is proposed to vary joint stiffness of a tendon-driven manipulator. The CEE is made of a cylindrical flexible object or rolling of a blade spring. By inserting the CEEs into routes of wires, the joint stiffness can be changed depending on a set of internal forces. However, it is difficult to obtain the solution of the inverse-kinematics for the manipulators with CEEs because of the complexity of CEE's deformation. This paper presents a numerical solving method for the inverse-kinematics of the one-link manipulator equipped with two CEEs. Using an approximate model of a CEE, we demonstrate the numerical solving method to obtain the wire length from the joint angle and stiffness. The precision of the proposed methods is evaluated by comparison between experimental results and numerical results.

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.

Generally, point-to-point control for a completely restrained (CR) parallel-wire-driven system requires a balancing internal force to prevent slackening of wires, along with a feedback term based on some displacement sensor. This paper specifically describes CR systems' internal force properties, then presents the possibility of motion convergence at a desired position when the internal force balancing at a position is given as sensorless feedforward input. Subsequently, we use the property of internal force positively for sensorless position control. This positioning method is applicable for low-cost manipulation, which does not require high accuracy, and for emergency positioning of systems when sensors malfunction.

A parallel-wire driven mechanism uses flexible wires instead of heavy rigid links. In this paper, we propose a robust point-to-point (PTP) position control method in the task-oriented coordinates for completely restrained parallel wire-driven robots, which are translational systems using the minimum number of wires under zero-gravity conditions. In the cases where parallel-wire driven robots are disassembled/assembled and used outdoors (also applied in space), actuator positions would be uncertain or contain some errors. The error of internal force among wires that results from such uncertainty of actuator positions deteriorates positioning performance. To overcome such a difficulty, adaptive compensation is employed for robust PD control against the error of internal force, in this paper. It is necessary for the adaptive compensation to separate the internal force term linearly into a regressor matrix and a parameter vector concerned with the errors of actuator positions. The internal force term, however, possesses the nonlinear characteristic concerned with the errors of actuator position. Noting the structure of the internal force term, this paper shows that measuring both the position of an end-effector and wire length in real time enables the linear separation. Not only does this robust PD control method ensure precise positioning using external sensors; it enhances the robustness for uncertainty of the Jacobian matrix, which results from the error of actuator installation. First, we explain the linearization of the internal force term. Next, the robust PD control for the parallel-wire driven system using the uncertain Jacobian matrix is proposed; then, we prove the motion convergence to desired points and discuss its robustness based on Lyapunov stability analysis. Finally, the usefulness of the proposed control method is demonstrated through experiments and simulations.

A parallel wire driven mechanism uses flexible wires instead of heavy rigid links. In this paper, we propose an adaptive position control method for the fully constrained parallel wire-driven systems that use the minimum number of wires under zero-gravity conditions. Unfortunately, conventional methods of adaptive control cannot be used directly because of the nonlinearity of an internal force term. To overcome such an impossibility, we incorporate a new idea into the conventional method to separate the internal force term linearly. Not only does this adaptive control method ensure precise positioning using external sensors; it enhances the robustness for uncertainty of the Jacobian matrix, which results from the error of actuator installation. First, we explain the linearization of the internal force term. Next, the adaptive control method for the parallel wire driven system using the uncertain Jacobian matrix is proposed; then we prove the motion convergence to desired points and discuss its robustness based on Lyapunov stability analysis. Finally, the usefulness of the proposed control scheme is demonstrated through both experiments and simulation

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.

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 Salle's 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 muscle's 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.

Multiple links structure/wire driven (tendon) system is one of robot structures, that has a serial-link structure driven by a wire-drive mechanism. It can be applied to a manipulator like elephant's trunk, a moray arm, the backbone of a humanoid, etc.. However, transformations among coordinates (task-oriented, joint-angles and wire-length) are very complicated. Therefore, it is not clear how sensory feedback control laws should be realized for the system. We propose a sensor-actuator map in which the sensor coordinates and actuator coordinates are arranged. By finding the paths from the sensor coordinates to the actuator coordinates on the map, the sensory feedback control laws can be built.

In this paper, we propose a new belt pulley mechanism based in rolling of a blade spring. This pulley can transmit wire tension by transforming itself into an ellipse. By inserting the belt pulleys into the route of wires instead of a complex mechanism, we can easily actualize a flexible tendon-driven manipulator. Internal force among the wires can drastically change the joint stiffness of the manipulator. Compared to other tendon-driven manipulators with variable stiffness, the belt pulley has some advantages such as simple design, low cost, less friction and light weight. Based on the characteristics of belt pulley, we developed tendon driven manipulators. The angle dependency is investigated experimentally though a one-link tendon manipulator. In order to overcome the difficulty of short stroke, we improved the belt pulley and developed a two-link manipulator.

A new type of robot system supported by wire cable drive actuators is proposed in order to convey heavy load, or to lighten the mass and inertia of the system. This system has a serial-link robot and wire drive actuators. The robot and the wire drive actuators are connected by wire cables. In this paper, a two-links robot with a wire cable is investigated as the first report. In this system, both joint torque and wire tension must cooperate together, so a control scheme in the task oriented coordinates for the robot is also proposed. The motion stability is analyzed by using a Lyapunov function. Finally the usefulness of the proposed system is demonstrated through some simulation results.

A type of robot system for a force display which has a serial link structure driven by a parallel mechanism is proposed. This system consists of serially jointed links as usual industrial robots and parallel wires that are driven by actuator units located on the base. Therefore, there are advantages of both a serial link structure robot and a parallel wire driven robot, such as large work space, high speed, safety, and so on. In previous analysis of the parallel wire driven mechanism, the object is a single rigid body, so that the driving principle in such cases was obvious based on a theory of "vector closure". However, in the case of the proposed system, the driving principle has not been cleared, because the mechanism is very complicated. In this paper, we show static conditions for each joint of the proposed system based on partial vector closure corresponding to the serial link structure/parallel wire system. Moreover, a prototype of the force display system is developed by using the conditions. Finally, the usefulness of the proposed system is demonstrated through some experimental results.