This paper proposes an iterative learning control scheme for a redundant manipulator to acquire a skilled hand writing motion of its end-point specified on an arbitrary smooth surface. Firstly, the existence of a unique solution to the Lagrange equation of motion of the robot, whose end-point motion is coincident with a given desired end-point trajectory described in Cartesian coordinate system, is shown theoretically. Second, the iterative learning control signal that enables the robot end-point to trace a desired trajectory specified on an arbitrary smooth surface with fulfilling a desired contact force is designed. Next, a numerical simulation for the iterative learning scheme is conducted to show the effectiveness of the proposed controller, and its result is compared to a theoretically derived desired joint angle trajectory. This comparison shows that there exists a unique solution of the desired joint angle trajectory when an initial pose of the manipulator and a desired end-point trajectory on the constraint surface are given, even under the existence of holonomic constraint and joint redundancy.

This paper proposes a novel object manipulation method to regulate the position and attitude of an object in the task space with dynamic stability by using a triple soft-fingered robotic hand system. In our previous works, a dynamic object grasping method without use of any external sensing, called “the Blind Grasping”, has been proposed. Although stable grasping in a dynamic sense has been realized by the method, a simultaneous object position and attitude control has not yet been treated, so far. In this paper, instead of using any information of the real object position and attitude, virtual data of object position and attitude are introduced by defining a virtual frame. By using the virtual information, a control signal to regulate the virtual object position and attitude without use of any external sensing is designed. The usefulness of our proposed control method even under the existence of nonholonomic rolling constraints is illustrated through a numerical simulation result.

A Riemannian-geometry approach for control and stabilization of dynamics of object manipulation under holonomic or non-holonomic (but Pfaffian) constraints is presented. First, position/force hybrid control of an endeffector of a multi-joint redundant (or nonredundant) robot under a nonholonomic constraint is reinterpreted in terms of ldquosubmersionrdquo in Riemannian geometry. A force control signal constructed in the image space spanned from the constraint gradient can be regarded as a lifting in the direction orthogonal to the kernel space. By means of the Riemannian distance on the constraint submanifold, stability on a manifold for a redundant system under holonomic constraints is discussed. Second, control and stabilization of dynamics of two-dimensional object grasping and manipulation by using a pair of multi-joint robot fingers are tackled, when a rigid object is given with arbitrary shape. Then, it is shown that rolling contact constraint induce the Euler equation of motion in an implicit function form, in which constraint forces appear as wrench vectors affecting on the object. The Riemannian metric can be introduced in a natural way on a constraint submanifold induced by rolling contacts. A control signal called ldquoblind graspingrdquo is defined and shown to be effective in stabilization of grasping without using the details of information of object shape and parameters or external sensing. The concept of stability of the closed-loop system under constraints is renewed in order to overcome the degrees-of-freedom redundancy problem. An extension of Dirichlet-Lagrange's stability theorem to a system of DOF-redundancy under constraints is presented by using a Morse-Lyapunov function.

A new robot hand dynamics model with rolling constraints and with a saddle joint at one finger is proposed, where two saddle-joint actuations are considered to be orderless. Spinning motion around the opposition axis connecting two center points of each finger-tip contact area with an object is faithfully treated, and a viscosity model for damping rotational motion of the object is proposed. A class of control signals without referring to object kinematics or using external sensing is proposed. Finally, numerical simulation results show the stability of motion of the overall closed-loop dynamics supplied with the proposed control input.

This paper proposes a stable object grasping method to realize dynamic force/torque equilibrium by using a triple robotic fingers system with soft and deformable hemispherical fingertips. In the authors' previous works, ¿Blind Grasping¿ control scheme, which realizes stable object grasping without use of any external sensing such as vision, force, or tactile sensing in the case of using a pair of robot fingers, has been proposed. This control methodology is based on a unique configuration of human hand, called ¿Fingers-Thumb Opposability¿. In this paper, a ternary finger in addition to a pair of fingers is introduced not only to expand a stable region of grasping, but also to enhance dexterity and versatility of the multi-fingered robotic hand system. To this end, a ¿Blind Grasping¿ manner is modified in order to install it in the triple fingers system. First, dynamics of the triple robotic fingers system and a grasped object with considering rolling constraints is modeled, and a control input based on the blind grasping manner is designed. Next, the closed-loop dynamics is derived and a stability analysis is shown briefly. Finally, its usefulness is discussed through numerical simulation results.

Modeling of pinching an object with arbitrary shape by a pair of robot fingers with hemispherical ends in a horizontal plane is proposed in a mathematical and computational manner. Since the curvature of an object contour with an arbitrary curve is variable according to the change of the contact point between the object surface and the rigid finger tip, the arclength paremeter ¿s¿ explicitly appears in the overall fingers-object dynamics. It is shown that the overall fingers-object system should be accompanied with the first-order differential equation of the parameter ¿s¿ that includes the curvatures of both the object contour and finger-tip curve. A control input, which is of the same category as the control input called ¿blind grasping¿ appeared in our former papers, is utilized for the realization of stable grasp. The control input does neither need to use the kinematic information of the object nor use any external sensing. Finally, numerical simulations are carried out in order to confirm the effectiveness of our proposed model and control input.

This paper focuses on the Riemannian distance and its application to skilled-motion plannings for the system. The Riemannian distance from one pose to another and vice versa is defined as the minimum curve-length measured by the Riemannian metric based upon the system inertia matrix among all curves connecting the two poses. The minimum-length curve in this meaning is called “geodesic” and reflects a movement of the system affected only by inertia-tensor-originated force (i.e., pure inertia, centrifugal, and Coriolis forces). In order to investigate in detail such a movement along the geodesic, some computer simulations are conducted in the cases of planar motions by a 4-DOF robot arm and biped walkings by a wholebody robot. It is shown through simulation results that movements attaining the Riemannian distance (natural movements in inertial actions) in the two cases tend to be similar to those in human skilled motions when human-scale robot models are chosen. Based upon the Riemannian distance, motion plannings for multi-body systems using physical properties inherent in their own physical structures are discussed.

This paper proposes an iterative learning control method for simultaneous force/position tracking tasks by using a 5 D.O.F. robotic thumb under non-holonomic rolling constraints. In our previous works, "blind touching", which is defined as a point-to-point control scheme for the robot to realize a desired contact position and a contact force simultaneously without any external sensing, have proposed. In this paper, an iterative learning control manner to realize a desired continuous trajectory of the center of the contact point together with a desired contact force on the task plane is proposed. The usefulness of this learning control method is demonstrated by showing results of computer simulations.

This paper shows through faithfully deriving Paffian forms of 3-D rolling contact constraints that 3-D pinching can be stabilized by using a pair of robot fingers with a hemispherical soft tip and minimum degrees of freedom under the gravity effect. The proposed control input is based on fingers-thumb opposition without using object information or external sensing. Stability analysis of the closed-loop dynamics is presented by using a Lyapunov method. Finally, for the sake of confirmation of effectiveness of the proposed control signals, numerical simulations are carried out.

This paper deals with dynamic object grasping and position control by using a triple-fingered robotic hand with hemispherical soft finger tips. In the authors’ previous works, a dynamic object grasping method by using a pair of robotic fingers, called “Blind Grasping”, which does not need to use any external sensing, was proposed. However, it is so far assumed that spinning motion around the opposite axis for the object does not occur during the overall movement, because there are only two opposite fingers and thereby the spinning is uncontrollable. By introducing a surplus finger in addition to a pair of robotic fingers, this spinning can be inhibited and it becomes controllable. In this paper, a triple-fingered robotic hand system with nonholonomic constraints is modeled, and a control input to realize stable grasping with object position control is proposed. Its usefulness and effectiveness are illustrated through some numerical simulation results.

This paper proposes an iterative learning control (ILC) scheme for a class of redundant robot arms to acquire the desired control input signals that produce an endpoint trajectory specified in task space. The learning update law of control input signals is constructed only in task space by modifying the previous control input through adding linearly an endpoint velocity trajectory error. Although the dimension of the task space is strictly less than the DOF (degrees-of-freedom) of the robot arm, the proposed method need neither consider any inverse kinematics problem nor introduce any cost function to be optimized and to determine the inverse kinematics (or dynamics) uniquely. Convergence of trajectory trackings to the specified one is shown by numerical simulations in both cases 1) free-endpoint motion and 2) constraint-endpoint motion with specified contact force. A theoretical proof of convergences in the case of free-endpoint motion is given on the basis of an approximated dynamics linearized around a desired solution in joint state space.

Human can pinch or grasp and manipulate an object stably and dexterously. Accomplishment of such tasks is contributed from human hand's configuration, called "fingers-thumb opposability". This opposability of the thumb against other digits is specific and granted to only human among primates. When we use a cell phone, or change a TV's channel using a remote controller, we grasp it by a palm and digits other than the thumb, and push buttons using the thumb quickly, without looking the buttons. These kinds of thumb's movement seem to be one of the most intelligent movements in a human. Therefore, execution of such touching tasks without visual or tactile sensing is called in this paper "blind touching". The goal of this research is to realize humanlike "blind touching" by means of a 5 D.O.F. thumb robot model with soft and hemispherical finger-tip. To do this, we formulate a simultaneous contact position and touching force control by using 3-dimensional rolling contact with the task plane. First, dynamics of the 5 D.O.F. thumb robot model with hemispherical soft finger-tip under rolling constraints is derived. Then, a sensory-motor control law without vision, force or tactile sensing is proposed. Some numerical simulations show that the desired contact position and touching force can be attained by the proposed control scheme. A theoretical proof of convergence to the desired state is also presented.

This paper is concerned with construction of a mathematical model for a class of lumped-parameter dynamics of a pair of robot fingers with soft and deformable tips pinching a rigid object. It is then shown that, in the case of a pair of planer fingers with two and three joints and a 2D rigid object with parallel or non-parallel flat surfaces, there exists a sensory-motor coordinated control signal constructed by using only the knowledge of finger kinematics and measurements of finger joints such that it realizes secure grasping in a dynamic sense. This shows that a pair of robot fingers can grasp a thing securely in a blind manner. The result is further extended to the case of 3D object grasping and manipulation by a pair of soft fingers, one of which can move in 3D space.

In the previous paper, a zero moment manifold (ZMM) control method effective for control of a class of under- actuated robots has been proposed and some experimental results have been presented. However, those robots can exercise only in the sagittal plane. This paper extends this control method to generate three-dimensional motion for gymnastics robots having an ankle joint pivoted at the floor. This ankle joint has two orthogonal axes and can not be actuator-driven. In order to maneuver intrinsically unstable robots, we propose an extended ZMM method by redefining the ZMM as a (n-2)- dimensional configuration manifold for such robots with n joints. It is shown theoretically that any motion toward the target pose starting from any given posture on a subset of the ZMM can be stabilized by using this extended ZMM control. Some simulation results of a 10-DOF gymnastics robot model show the effectiveness of the proposed method.

To date, many control algorithms have been proposed for grasping of rigid objects, through fixed or rolling contact in a horizontal plane, by multi-fingered robotic hands. However, these non-sliding contacts have not been considered in a single model. Here, by examining kinematics, we present a single model for both fixed and rolling contacts in a horizontal plane, and show that a fixed point contact is a special case of a rolling contact. We introduce a blind grasping algorithm with tactile feedback, and show that the algorithm does not require any tactile feedback in the case of a fixed point contact. Numerical simulations show that the algorithm is applicable for stable pinching with non-sliding contact

A natural control method for resolving the ill-posedness of inverse kinematics of multi-joint reaching movements under redundancy of degrees-of-freedom (DOF) is proposed, which need neither introduce any artificial performance index to determine the inverse kinematics uniquely nor calculate the pseudo-inverse of the Jacobian of task coordinates with respect to joint coordinates. The control signal is composed of a linear superposition of three terms 1) damping shaping, 2) task-space position-error feedback with a single stiffness parameter, and 3) gravity compensation by means of estimation of uncertainty in the potential energy. It is shown through computer simulation and theoretical analysis that the signal can generate smooth reaching motions provided that a set of damping factors and the stiffness parameter are selected in a synergistic way. A physical meaning of each term of the control signal is discussed from the viewpoints of Newtonian mechanics and neuro-physiology. It can be concluded from those illustrative examples that mechanical robots with redundant DOFs can be endowed by synergistic adjustments of damping factors and a stiffness parameter with ability of generating human-like skilled motions

A mathematical model expressing motion of a pair of multi-DOF robot fingers with hemi-spherical ends grasping a 3-D rigid object with parallel flat surfaces is derived together with non-holonomic constraints. By referring to the fact that human grasp an object in the form of precision prehension dynamically and stably by opposable forces between the thumb and another finger (index or middle finger), a simple control signal constructed from finger-thumb opposition is proposed and shown to realize stable grasping in a dynamic sense without using object information or external sensing (this is called "blind grasp" in this paper). Stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of the concept called "stability on a manifold". A few of preliminary simulation results is shown to verify the validity of theoretical results

This paper proposes a novel control method for enabling a robotic arm with a pair of two degrees of freedom (DOFs) robotic fingers as an endeffector to execute a variety of superimposed tasks in a coordinated way. Differently from conventional control methods previously proposed for arm-hand systems that adopt a separate design to control each robotic part, such as an arm and a hand, the proposed method designs a unified single control scheme for movements of the whole robotic arm-fingers system. It is assumed that finger-tips are rigid and there are rolling constraints between the finger-tips and a target object, and whole motion of the system is confined to the horizontal plane. The results of numerical computer simulation demonstrate that the proposed method is sufficiently effective in control of such a whole robotic arm-fingers system, and in particular is applicable to superimposed tasks, grasping, regulating, and replacing a target object through composition of the control signal based on the principal of superposition. It is concluded that special attentions in design of control signals should be paid to the wrist that combines the finger parts to the arm

In this paper we propose a control method for pinching an arbitrary shaped object in a horizontal plane by a multi-fingered robot with arbitrary shaped fingertips. It is assumed that the constraints between the fingertips and the object is rolling and smooth, and each finger contacts with the object at one point. The controller requires no object information, no preplanning and no force sensors. It uses only joint angle, joint angular velocity, contact points (tactile feedback) and the fingers' link length. The paper begins with an derivation of dynamics of the finger-object system. We propose a stable grasping controller and briefly show the proof of dynamic stability of the system. The simulation results demonstrate that the controller with tactile feedback can easily realize the stable pinching

In this paper, we study co-activation of digitorum muscles while perform stable pinching and posture regulation tasks of an object by using dual fingers. The fingers have 2 D.O.F. joints and are actuated by nonlinear redundant digitorum muscles to mimic human-like pinching movements. Firstly, we illustrate the kinematics and the dynamics of the overall system, which consider not only the fingers and an object, but also three muscles for each finger to actuate the finger links. Secondly, we consider nonlinear muscle property based on several physiological studies, and propose sensory-motor control rule to the muscles in order to realize stable pinching simultaneously with posture regulation by introducing internal force term induced by co-activation between flexor digitorums and extensor digitorums to modulate the damping factor in joint space. We verify our study by numerical simulations and conclude that this dual fingers system can realize human-like stable pinching and posture regulation

What is a decisive difference between a mechanical hand and our human hand? The robot hand can not be a versatile tool, even if it is made to resemble a human hand exactly in shape and mechanism. Even at the present age of robotics, it is too clumsy to perform a variety of ordinary tasks that a human encounters in his or her everyday life. In this talk, I claim that the clumsiness manifests the lack of knowledge of everyday physics and as well the lack of scheme for designing an artificial CNS (Central Nervous System) for the robot so as to cope with its sophisticated interplays with environments of everyday tasks. Here, the term "everyday physics" is used as a scientific domain related to accountability of dexterous accomplishment of ordinary tasks through manipulating things, with or without sensing and with or without consciousness as seen in ordinary human life. Here, design of an artificial CNS should be a domain of science and engineering that should be called "human robotics". It should be a core of robotics that attempts to unveil secrets of human intelligent behaviors from perception to action and vice versa. Actually, a half century ago N. A. Bernstein was enthusiastic in perceiving the mysteries of dexterity of human movements with redundant Degrees-of-Freedom regardless of illposedness of inverse kinematics.

Object manipulation involves two tasks: 1) reaching to grasp and 2) grasping. Motivated by physiological studies related to first task, reaching to grasp, and a simple control method composed of joint-damping and a single virtual spring, we try out for realization of continuous reaching-to-preshaping movements by robotic hand-arm systems with a pair of 2DOFs fingers and a 3DOFs arm. Differently from conventional researches, this paper treats neither trajectories planning nor inverse kinematics. Instead of those, a few transit point and desired point in task-space and a control method with Jacobian transpose and joint velocity damping are merely adopted. Based on results of numerical computer simulation, it is concluded that it is possible to simply realize continuous robotic reaching-to-preshaping motion by adopting the transit points and the control method

This paper aims to formulate a dynamic model of a pair of dual multi-DOF robotic fingers with rigid tips grasping a circular-shaped object and proposes a new control framework for dexterous manipulation. Firstly, based on the Lagrange method and Hamilton's principle, a dynamic model of the general object-fingers setup has been described as a system of algebraic differential equations composed of ordinary differential equations governing dynamics of the fingers and the object and a set of algebraic constraints governing rigid contacts between two finger-tips and object' surfaces. Secondly, a control algorithm for stable grasping of the object by the pair of fingers has been proposed. Thirdly, another control signal for desired orientation and position of the grasped object has been proposed and asymptotic convergence of the closed dynamics to the desired orientation and position has been analysed. The principle of linear superposition of control signals has been applied to ensure stale grasping while control desired motion. Numerical simulation results have reconfirmed the effectiveness of the proposed control law.

The purpose of this paper is to construct a supervisory control system for telemanipulation with a robotic hand and to introduce some basic strategies of the systems and a case study, in which a robotic hand in a remote side does preshaping, grasping and orienting. The developed master system is designed based on human pinching motion and can be manipulated intuitively. The feedback loop of the developed slave system is closed at the slave side and the slave robotic hand can manipulate an object reliably. Three experiments are executed to confirm that the slave hand pinches objects and controls their orientation angle easily even time delay exists

A simple control method for redundant multi-joint arm was proposed recently by gaining a physical insight into human multi-joint reaching movements in redundancy of DOFs. Differently from the traditional approaches, the method need neither introduce any artificial performance index to resolve kinematics ill-posedness nor calculate the pseudo-inverse of the Jacobian matrix of task coordinates with respect to joint coordinates. This novel approach is based upon an idea of "virtual spring-damper hypothesis," and the control signal is composed of linear superposition of three terms 1) joint-damping, and 2) virtual damper effects in parallel to 3) virtual spring effects in task space. This paper shows through experiments by using an industrial robot arm (PA-10) with redundant multi-joints that the control signal can generate smooth reaching motions without incurring any annoying self-motion even in the case of industrial robots. It is shown further that virtual damping effects in task space play an important rule in making endpoint trajectories of the robot arm quasi-straight. Consequently, the control signal can easily generate human-like multi-joint movements of robot arms without spending a huge amount of computational cost for motion plannings of not only the endpoint trajectory but also each joint trajectory

This paper focuses on a sensory-motor control mechanism in human reaching movements from the perspective of robotics. By formulating a musculo-skeletal redundant system which takes into account a nonlinear muscle property and performing numerical simulations, we suggest that the human-like reaching movements can be realized by using only simple task-space feedback scheme together with the internal force effect coming from nonlinear property of muscles without any complex mathematical computation such as an inverse dynamics or some optimal trajectory derivation. Firstly, we introduce both kinematics and dynamics of a three-link serial manipulator with six monoarticular muscles and three biarticular muscles model whose movements are limited within a horizontal plane. Secondly, the nonlinear muscle property coming from a physiological study based on Hill's muscle model, is taken into consideration. This nonlinearity makes it possible to modulate the damping effect in joint-space by considering the internal force generated by the redundant muscles. By utilizing this feature, the end-point converges to the desired point using only simple task-space feedback control scheme, even thought the system owns both the joint and muscle redundancies. Finally, we illustrate numerical simulations to show the effectiveness of the control scheme, and suggest one of the direction to study brain-motor control mechanism of human movements

Construction of a numerical simulator for 3-D object pinching and manipulation by a pair of robot fingers is described on the basis of mathematical modeling of physical interactions between the object and fingers under rolling constraints. The simulator consists of a set of Lagrange's equations of motion of the fingers and object under holonomic constrains and differential equations of nonholonomic constraints expressing rotational motion of the rigid object. By using this simulator, numerical simulation results of 3-D object pinching and manipulation under the gravity effect based on the control law rendering "blind grasping" without use of any object kinematics or any external sensing is carried out in a case that the fingers have excess joints. Numerical results show the validity of mathematical modeling and the control method

This paper is aiming at showing a sensory-motor coordination control scheme that realizes stable pinching of rigid objects with parallel or non-parallel flat surfaces movable in 2-dimensional vertical plane by a pair of robot fingers with hemispherical ends. The proposed control signal is composed of gravity compensation for fingers, damping shaping, exertion of forces to the object from opposite directions, generation of moments for rotational moment balancing, and regressors for estimating unknown steady-state terms, all of which neither need the knowledge of object parameters nor use any object sensing data. In other words, stable grasping can be realized by using only finger joint sensing in a blind manner without using force sensors and tactile sensing. Stability of pinching motion with convergence to the state of force/torque balance is shown through computer simulations and is also proved theoretically.

This paper explores what are the fundamentals of physiologically inspired robot control through investigating two interesting but difficult problems of robot control: 1) robotic hand-writing and 2) human-like multi-joints reaching with surplus DOFs (Degrees-of-Freedom). Noteworthy characteristics in control of these systems are a) redundancy in DOF, b) dexterity in execution of a target task, and c) indispensability of sensory motor coordination, though d) their dynamics are nonlinear and governed by geometric constraints in the former system. Due to these inherent characteristics, both the conventional control-theoretic approaches originated from the linear system theory and Lyapunov’s direct method are likely to fail. By gaining physical insights into these nonlinear dynamics with redundancy in DOFs and physical descriptions of target tasks, this article claims that a symbiosis of “robotics” and “neuro-physiology” is quite effective in finding a simpler sensory-motor coordination principle in each case that leads to accomplishment of each imposed task regardless of annoying characteristics of its motion dynamics with nonlinearity and DOF-redundancy.

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.

This paper proposes a novel control scheme called the ZMM control for generating any desired motion for a family of under-actuated robots with instability such as gymnastics robots and robotic puppets having a single leg pivoted at the floor. This pivotal ankle joint can not be actuator-driven but is passive with a rotary damper. Therefore these robots are intrinsically unstable under the effect of gravity and considered to be under-actuated and nonholonomically constrained. This paper first introduces an (n $1)-dimensional configuration manifold for such robots with n joints, which is called the ZMM (zero moment manifold) and defined as a set of joint angle vectors that satisfy zero of the rotational moment around the first ankle joint. It is shown theoretically that any motion starting from any given posture on a subset of the ZMM and targeting to a desired form of motion can be stabilized by using a coordinated control composed of gravity compensation for other joints except the ankle and PD feedback despite of the existence of a nonholonomic constraint. The effectiveness of the proposed control scheme is verified by numerical simulation and experimental results using a gymnastics robot with four joints.

A simple control method for 3-dimensional multi-joint reaching movements under redundancy of Degrees-of-Freedom is proposed, which neither need to introduce any performance index to solve inverse kinematics uniquely nor need to calculate pseudo-inverse of the Jacobian matrix of task coordinates with respect to joint coordinates. The proposed control signal is composed of linear superposition of three terms: 1) angular-velocity feedback for damping shaping; 2) task-space position feedback with a single stiffness parameter; and 3) compensation for gravity force on the basis of estimates for uncertain parameters of the potential energy. Through a variety of computer simulations by using a whole arm model with five DOFs, the importance of synergistic adjustments of damping factors as well as its relation to selection of the stiffness parameter is pointed out. It is shown that if damping factors are chosen synergistically corresponding to the inertia matrix at the initial time and the stiffness parameter then the endpoint converges asymptotically to the target position and reaches it smoothly without incurring any self-motion.

The human thumb is a digit playing crucial roles among all digits used for object manipulation. The special feature of the thumb termed as pad-opposition with four other fingers makes human precision manipulation possible, and during four other fingers dexterously manipulate an object, the thumb functions as a role of stably supporting other's manipulating motions within the pad-opposition. In various characteristics of the thumb, this paper concentrates upon the large radius of pad curvature of the thumb compared with those of four other fingers. In order to show the influence of the large pad curvature, it is shown that reiterated fingering motions can be easily generated by a pair of robotic fingers with different pad curvatures by using a sensory-feedback. It is also shown from computer simulations and experiments that the pad curvature automatically distinguishes roles of the thumb and other fingers. It is finally claimed that the thumb plays a crucial role in enhancement of dexterity and stability in such an implicit manner that it helps the other opposite finger with less pad curvature to make a swift rolling movement that can sophisticatedly control movements of the object.

This paper proposes a manipulation of a circular object in a horizontal plane by a pair of finger robots. This method guarantees the dynamic stability of the system and does not require any object models and object sensing to manipulate the object. It is assumed that there is no friction between the object and the horizontal plane and it is possible to be adequately large friction between the fingertips and the object. We examine the condition of stable grasping of a circular object and propose controllers for stable grasping and for controlling its approximate relative orientation angle without object sensing. The experimental results show the validity of the controller.

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.

This paper proposes a method for controlling an object with parallel surfaces in a horizontal plane by a pair of finger robots. The control method can achieve stable grasping, relative orientation control, and relative position control of the grasped object. The control inputs require neither any object parameters nor any object sensing, such as tactile sensors, force sensors, or visual sensors. The control inputs are also quite simple and do not need to solve either inverse kinematics or inverse dynamics. The stability of the closed-loop system is proved, and simulation and experimental results validate the control method.

In order to enhance dexterity in execution of robot tasks, a redundant number of degrees-of-freedom (DOF) is adopted for design of robotic mechanisms like robot arms and multi-fingered robot hands. However, it is said that surplus DOF of such a robotic mechanism incurs a problem of illposedness of inverse kinematics from task space to joint space. This paper attempts to show that, in the case of a pinching task (grasping in 2D space), by using a pair of robot fingers with hemispherical finger ends, 1) the ill-posedness of inverse kinematics can be resolved in a natural and dynamic way by means of sensory feedback signals from task space to joint space and 2) surplus DOF of fingers enhances dexterity of pinching tasks by remarkably accelerating the speed of convergence toward target object manipulation.

This paper proposes stable grasping and angle control methods of an object with parallel surfaces by a pair of finger robots in a horizontal plane. The method does not require any object sensing for stable grasping but only measurement of the object angle for absolute orientation angle control. Relative orientation angle control of the grasped object can be attained even if no object sensing is used.

In order to enhance dexterity of a robot hand, it is said that the robot should be designed to have a redundant number of degrees-of-freedom(D.O.F.). However, in redundant robotic systems, the inverse kinematics from task-description space to joint space becomes ill-posed. Therefore the problem of determination of joint motions becomes difficult. In order to avoid this ill-posedness, many methods have been proposed, most of which introduce an additional input term calculated from minimization of some intentionally introduced artificial index of performance. This paper treats a 4-D.O.F. redundant handwriting robot by using a novel and simple control method and resolving the problem of such ill-posedness based on sensory feedback in task-coordinates. The effectiveness of the proposed control method is demonstrated through computer simulation and a preliminary experiment.

This paper is aiming at clarifying the relationship between the total DOF (degree-of-freedom) of a pair of robot fingers and feasibility of concurrent control of stable grasping and object manipulation of a rigid object with non-parallel surfaces under the gravity effect. It is shown by computer simulation and an actual experiment that if each finger has at least two DOF then the desired task of concurrent stable grasping and posture control can be attained, though the use of a pair of dual fingers with three DOF is shown theoretically to be feasible. It is also shown that the use of fingers with single DOF and two DOF cannot be feasible under the gravity effect, though it is feasible in the case that the gravity effect can be ignored.

This paper is concerned with a stability theory of motion governed by Lagrange's equation for a pair of multi-degrees of freedom robot fingers with hemispherical finger ends grasping a rigid object under rolling contact constraints. When a pair of two DOF. fingers is used and motion of the overall fingers-object system is confined to a plane, it is shown that the total degree of freedom of the fingers-object system is redundant for realization of stable grasping though there arise four algebraic constraints. To resolve the redundancy problem without introducing extra performance specifications, a concept of stability of motion starting from a higher dimensional manifold to a lower-dimensional manifold expressing a set of states of stable grasp with prescribed contact force is introduced and thereby it is proved in a rigorous way that stable grasp in a dynamic sense is realized by a sensory feedback constructed on the basis of measurement data of finger joint angles and the rotational angle of the object. Further, it is shown that there exists an additional sensory feedback that realizes not only stable grasp but also orientation control of the object concurrently. These results can be extended to other two cases that: 1) motion of the overall system is confined to a vertical plane and therefore it is affected directly by the gravity; and 2) the object has non-parallel but flat surfaces.

Most research so far in robot control has assumed either kinematics or Jacobian matrix of the robots from joint space to Cartesian space is known exactly. Unfortunately, no physical parameters can be derived exactly. In addition, when the robot picks up objects of uncertain lengths, orientations, or gripping points, the overall kinematics from the robot's base to the tip of the object becomes uncertain and changes according to different tasks. Consequently, it is unknown whether stability of the robot could be guaranteed in the presence of uncertain kinematics. In order to overcome these drawbacks, in this paper, we propose simple feedback control laws for setpoint control without exact knowledge of kinematics, Jacobian matrix, and dynamics. Lyapunov functions are presented for stability analysis of feedback control problem with uncertain kinematics. We shall show that the end-effector's position converges to a desired position in a finite task space even when the kinematics and Jacobian matrix are uncertain. Experimental results are presented to illustrate the performance of the proposed controllers.

For a class of robot arms, a proportional-derivative (PD) controller plus gravity compensation yields the global asymptotic stability for regulation tasks, and some proportional-integral-derivative (PID) controllers guarantee local regulation without gravity cancellation. However, these controllers cannot render asymptotic stability for tracking tasks. In this paper, a simple decentralized continuous sliding PID controller for tracking tasks that yields semiglobal stability of all closed-loop signals with exponential convergence of tracking errors is proposed. A dynamic sliding mode without reaching phase is enforced, and terminal attractors, as well as saturated ones, are considered. A comparative experimental study versus PD control, PID control, and adaptive control for a rigid robot arm validates our design.

This paper shows the performance of dynamic motion of dual multi-degree of freedom fingers with soft tips in fine manipulation of an object via computer simulation. A mathematical model of its dynamics is described as a system of ordinary differential equations expressing motions of the fingers-object setup together with algebraic constraints due to tight area contacts between the finger tips and surfaces of the object. A constraint stabilization method is used for solving numerically the differential algebraic system, and a design principle of superposition of feedback control signals is applied for controlling the pinching motion of the dual multi-degree of freedom fingers with soft tips. Furthermore, problems of dynamic stable grasping, controlling object rotational angle and regulating position of the mass center of the object, are considered simultaneously and their performances in a coordinated fine motion are illustrated by simulation results.

Most researches so far on robot control have assumed that the exact kinematics and Jacobian matrix of the manipulator from joint space to Cartesian space are known. Unfortunately, no physical parameters could be derived exactly. In addition, the robot is required to interact with its environment and hence the overall parameters would change according to different tasks. In the paper, simple feedback control laws are proposed for setpoint control of robots with uncertain kinematics and dynamics. We show that the end-effector's position converges to a desired position in a finite task space even when the kinematics is uncertain.

This paper proposes a pair of single or multi-DOF robot fingers with soft and deformable tips that can pinch an object stably in a dynamic sense with the aid of real-time sensory feedback. To realize dynamic stable pinching, a practical method of using optical devices is proposed for measuring both the maximum displacement of finger-tip deformation and the relative angle between the object surface and each of finger links. It is shown that the overall closed-loop system of a pair of two single-DOF fingers with soft tips with real-time sensory feedback of the difference between the centers of two area-contacts at both sides of the object becomes asymptotically stable. This means that the pair achieves dynamic stable grasping (pinching). In the case of a pair of 1-DOF and 2-DOF fingers with soft tips, it is shown that the proposed method of closed-loop feedback of the difference between the centers of two area-contacts and the rotational angle of the object can establish not only dynamic stable grasping but also regulation of the posture of the object.

A new approach to the detection of the position and the orientation of planar motion objects based on one-sided Radon transform is presented. Detection of position and orientation of planar motion objects is a key to advanced object handling. First, one-sided Radon transform is introduced and its properties are investigated. Next, algorithms to detect planar motion of objects are constructed based on the properties of one-sided Radon transform. The algorithms are then implemented on a computer and are evaluated experimentally.

Dynamics of a robot naturally satisfy passivity whether its tool endpoint is free to move or constrained on a surface of an object or environment. This property is inherent to the fact that robot dynamics can be derived from a variational form based on the concept of Euler-Lagrange formalism. In this paper it is shown that a class of robust and effective controllers can be designed by referring to the variational form. These controllers have a simple structure and can be designed without using the exact knowledge of kinematic and dynamic parameters and thereby are robust against parameter uncertainties. It is also shown that a class of H/sub /spl infin// controllers for suppression of the effect of external disturbances can be designed naturally without solving Hamilton-Jacobi's differential equations. Even in the case of a system of dual fingers with flexible and soft finger-tips grasping a rigid or deformable object, the dynamics satisfy passivity, from which feedback control schemes can be devised for realizing dynamic stable grasping and dexterous manipulation.

We consider the position and force regulation problem for a soft tip robot finger in contact with a rigid surface under kinematic uncertainties concerning the contact point location and the direction of free movement. The reproducing force is related to the displacement through a non-linear function whose characteristics are unknown but both the actual displacement and force can be directly measured. An adaptive controller is proposed and the asymptotic stability of the force error and estimated position error under dynamic and kinematic uncertainties is shown for the planar case. Simulation results for a 3-degrees-of-freedom planar robotic finger are presented.

The paper is aimed at presenting a physical interpretation of practice-based learning (so-called "iterative learning control") for robotic tasks from the viewpoint of "bettering impedance matching". At first, the concepts of impedance and impedance matching that are inherent to linear electric circuits are generalized for a class of nonlinear dynamics including robotic tasks by means of passivity. It is then shown in the simplest case when the tool endpoint is free to move that a simple iterative scheme of learning enables robots to make a progressive advance in a sense of zero-impedance matching at every trial of operation. In the case of impedance control when a soft and deformable finger-tip presses a rigid object or environment, it is shown that, for a given desired periodic force, physical interaction between the soft fingertip and the rigid object, the robot learns steadily the desired task by monotonously increasing the grade of impedance matching pertaining to dynamics of the robot task with controller dynamics.

Most research so far on robot control assumes that the kinematics and Jacobian matrix of the manipulator from joint space to task space are known exactly. This assumption leads to several open problems in the literature of robot control and limits the potential research and applications of robots. In this paper, we present an approximate Jacobian PID control law for set-point control of robot with uncertain kinematics from joint space to task space. Simulation results are presented to illustrate the results.

We consider the problem of force regulation for the physical interaction between the soft tip of a robot finger and a rigid object under kinematic uncertainties. It is assumed that the nonlinear characteristics of the reproducing force and the finger dynamic parameters are unknown and that the kinematic uncertainties arise from both uncertain robot finger kinematics and uncertain rigid object geometry. An adaptive controller is proposed and the asymptotic stability for the force regulation problem under dynamic and kinematic uncertainties is shown for the planar case. Simulation results for a 3-degrees-of-freedom planar robotic finger are presented.

This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback structure is analyzed and convergence of the proposed learning update law after a sufficient number of repetitions is proved. It is shown that this convergence realizes impedance matching in a sense of electric circuit theory of the feedback system can be expressed as a lumped-parameter electric circuit. The iterative learning control scheme is then applied for a case of impedance control of robotic tasks when the characteristics of reproducing force of the deformable material is nonlinear in its displacement and unknown and the tool mass is uncertain. Simulation results are also presented, which show effectiveness of the proposed learning control scheme.

This paper aims to generalize the concept of impedance matching to cope with nonlinear dynamics that govern robotic systems with or without constraints. In the case of single degree of freedom dynamics the impedance matching is first introduced by the concept of balancing between load impedance and internal impedance. Generalization of this concept to the case of multiple degrees of freedom robots can be fulfilled implicitly via a coordinates transformation by constructing a negative feedback connection of two hyper-stable blocks (one is a parameter estimator and positioning and the other is a position and force regulator).

Most research on multifingered robot control has assumed that the Jacobian matrices from joint space to task space is exactly known. This implies that the locations of contact points, geometry of the object, kinematics of the multifingered robot hands must be exactly known. In this paper, a task-space feedback control problem of multifingered robot hands with uncertain Jacobian matrices is formulated and solved. The stability and robustness of the proposed controllers to the uncertainties in Jacobian matrices are analyzed.

Research of robotics aims to realise some aspects of human functions into mechanical system. Human can manipulate things skilfully without the exact knowledge of both dynamics and kinematics of arms. Our arms are also able to overcome singular position by moving along it or passing through it. The exploration of a robot controller to cope with the uncertainties in both dynamics and kinematics is an important step towards understanding the dextrous movement of mechanical systems. In this paper, simple feedback control laws are proposed for setpoint control of robots with uncertain kinematics and dynamics. We shall show that the end-effector's position converges to the desired position in a finite task space even when the actual Jacobian matrix is singular.

Robot dynamics under static and Coulomb frictions are shown to be equivalent to a nonlinear position-dependent circuit including a set of on-off switches and analyzed as a variable structure system. It is shown that, under the existence of static and Coulomb frictions at each joint, an ordinary PD feedback with gravity compensation, for set-point position control leads to a trapping of motion at some immovable state within a finite time without reaching the given target position. On the contrary, by introducing regressors for uncertain parameters of gravity forces and static and Coulomb frictions it is possible to show that a proper update law of such regressors together with an adequate PD feedback renders the target state of the robot system globally, asymptotically stable without incurring any offset and without measuring any force/torque signals. It is shown that regressors can be treated as an operator with positivity and thereby regarded as a time-varying capacitor. These observations suggest a proposal of robots that are subject to only the law of inertia, that is, a proposal of inertia-only robots or gravity/friction-free robots.

Learning control and model-based adaptive control schemes are proposed for coordinated control of multiple manipulators when physical parameters such as inertia moments and position of the mass center of a target object are unknown. The proposed schemes are constructed on the equation of motion of the coordinated multiple manipulators, which is derived in terms of joint angle coordinates of manipulators and includes the dynamics of the target object. Since this equation does not explicitly include the position of the mass center of the object, the proposed schemes can achieve exact tracking of given joint angle trajectories and given trajectories of "internal force" without using physical parameters of the manipulators and the object. The effectiveness of the proposed schemes is illustrated by the numerical simulation results on a model of two 3-DOF manipulators holding a single object.

Learning control and model-based adaptive control schemes are proposed for coordinated control of multiple manipulators holding a geometrically constrained object whose physical parameters such as inertia moments and position of the mass center are unknown. The proposed schemes are constructed on the equation of motion of the coordinated multiple manipulators which is derived in terms of joint angle coordinates of manipulators and includes the dynamics of the target object. Since this equation does not explicitly include the position of the mass center of the object, the proposed schemes can achieve exact tracking of given joint angle trajectories and given trajectories of "internal force" and external constraint force without using physical parameters of the manipulators and the object.

This paper reports comparative experiments with a new model-based adaptive force control algorithm for robot arms. This controller provides simultaneous position and force trajectory tracking of a robot arm whose tool tip is in point contact with a smooth rigid surface. The algorithm is provably stable with respect to the commonly accepted rigid-body nonlinear dynamical model for robot arms. Comparative experiments show the new adaptive model-based controller to provide performance superior to that of both nonmodel-based controllers and nonadaptive controllers over a wide range of operating conditions.

Two decentralized cooperation controllers are presented for trajectory tracking of two manipulators handling an object. The controllers control positions of the robots distributively by using trajectory errors of the object in the task space. In the first controller, the internal force between the object and the manipulator is controlled only by feedforward of the desired force. The second controller uses a force feedback. No communication is required between the manipulators in the both controllers. Their globally and exponentially asymptotic stability are guaranteed by Lyapunov functions.

This paper deals with the decentralized control of multiple manipulators in a class of cooperative tasks called holonomic cooperation and presents two joint-space controllers. The controllers decouple the position and force control by the joint-space orthogonalization method. The first controller implicitly realizes the force control through a feedforward of the desired force, but the second one includes a loop of the force feedback. Their global and exponential stability is guaranteed by Lyapunov functions. Unlike centralized controllers, our decentralized controllers are designed in the joint spaces of individual robots so that the implementation does not need to manipulate high dimensional matrices.

Nonlinear H/sub /spl infin// optimal control theory has been well researched in previous years. However, proposed control schemes are quite complicated and hardly feasible in construction of a controller actually for robotic systems. As for robot control, it has been shown that the passivity concept plays a key role in constructing good controllers for dexterous motion with or without tool endpoint constraint. In this paper, the authors propose an H/sub /spl infin//-optimal tuning method for these controllers established on the passivity-based concept. By using the passivity concept, the conditions for H/sub /spl infin//-optimal tuning can be obtained in a simple and clear form. As an application of this H/sub /spl infin// control scheme, it is shown that, even for a trajectory tracking problem for a robot under geometrical endpoint constraint, model-based hybrid adaptive control methods previously proposed can be extended to cope with H/sub /spl infin// tuning in the sense of disturbance attenuation.

This paper presents a general approach of model-based adaptive hybrid control for holonomically constrained manipulators. Both time-invariant and time-variant constraints are dealt with. This controller works on the basis of an orthogonalization principle of trajectory and force errors in the joint space and intrinsic characters of the manipulator dynamics. Its asymptotic stability is guaranteed by a passivity-based Lyapunov function. A distributed cooperation controller based on the same concept for multiple manipulators is also presented. Further, we show a parallel implementation of this controller using transputers on a six DOF direct drive manipulator, and some experimental results.

A learning controller for coordination of multiple manipulators holding an object rigidly is proposed based on the principle of joint-space orthogonalization. It is devised by referring to (1) the fact that dynamics of multiple manipulators resembles that of a single robot under geometric endpoint constraint except the term of the sum of forces exerted from manipulators to the object, (2) the passivity of the dynamics of multiple manipulators, (3) the use of feedback signals of velocity error and force error which are projected to their respective manifolds to be orthogonal to each other in joint-space, and (4) the passivity of the total error system. The convergence of tracking position and force errors is proved theoretically provided that multiple manipulators hold the object rigidly and are initialized at the same posture at the beginning of each learning trial. The effectiveness of this controller is ascertained by computer simulation by using two manipulators with three joints.

Based on a novel error coordinate system which induces a sliding mode, two passivity-based adaptive controllers are proposed. The control strategies, similar to the one proposed by Slotine-Li (1987) for free motion and by Arimoto et al. (1993, 1994) for constrained motion, give rise to the exponential convergence of tracking errors. Computer simulation data for free and constrained motions show a high performance.

This paper reports comparative experiments with a provably correct model-based adaptive robot control algorithm for simultaneous position and force trajectory tracking of a robot arm whose gripper is in point contact with a smooth surface. The experiments show the new adaptive model-based offers performance superior to that of its non-model-based counterpart over a wide variety of operating conditions.

This paper presents a general approach for adaptively and distributively controlling multiple cooperative manipulators. The proposed approach does not adopt a centralized architecture but assigns a controller to each robot. Any communication requirement is determined by motion constraints existing in the cooperative system. All physical parameters of the manipulators or the load of the system are online estimated by a model-based adaptive algorithm. A Lyapunov function guarantees asymptotic convergence of trading errors of the trajectory and the interactive force among the robots. Performance of this controller is further shown by simulations on six DOF manipulators.

This paper presents an algorithm which leads a mobile robot to its destination in an environment where multiple obstacles are moving around. The movement of each obstacle is forecasted under the assumption that it moves with a piecewise constant velocity. Based on the forecast, a feasible path for a robot is searched in (x,y,t) space by a heuristic method. The robot follows feasible path for a while. This path is composed of straight lines and circular curves. To accommodate the actual changes in the obstacles' velocity against the forecast, the forecast and path searching is iterated frequently while the robot is moving to the goal. This paper examines the behavior obtained by different experimental conditions in the computer simulations.

An algorithm for finding the shortest path of a disc among a set of polygonal obstacles is presented. Let N/sub t/ denote the total number of obstacle vertices and N/sub c/ the number of convex vertices. Our algorithm uses a radius-independent data structure called extended tangent graph (ETG) which registers collision-free tangents of the obstacles according to different discs and takes O(N/sub c//sup 2/) space. The ETG depends only on original obstacles, and it is constructed in advance without using any information about the disc in O((N/sub c/+k)N/sub t/) computation time, where k is the number of outer common tangents of the obstacles. It takes O(N/sub t/logN/sub t/) time to partially update the ETG to reflect the start and goal of a given disc. The shortest path is planned by a graph-search algorithm.>

A learning control scheme for a class of robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two different signals updated separately at every trial by different ways. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the manipulator endpoint. Not only the uniform boundedness of position and velocity trajectory errors but also the uniform convergence of position and velocity trajectories to their desired ones with repeating practices are proved theoretically. In addition, it is shown that the contact force itself converges to the desired one in the sense of L/sup 2/-norm with repeating practices. Computer simulation results by using a 3 DOF manipulator are presented to demonstrate the effectiveness of the proposed method and to examine the speed of convergence of force trajectories besides position and velocity trajectories.>

We propose a robot controller for stably executing all phases of contact tasks involving discontinuities due to the introduction or removal of contact forces. Systems executing such tasks are characterized by ambiguous right hand sides in the differential equations of motion. Consequently, our approach develops upon ideas from generalized dynamical systems (GDSs), an orthogonalization principle, the Hertz contact model, and model-based adaptive control. The result is an asymptotically stable controller that discontinuously switches among three possible configurations based on the contact situation. The method is used to tune the controller independently for both position and constrained motion as also for reducing contact forces during the process of making contact with the environment. The underlying theory is first described. Then the controller synthesis and proof of its asymptotic stability, based on Lyapunov's method, are presented. The idea is illustrated with a simulation of a simple task.>

A learning control scheme for robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two signals updated separately at every trial by different laws. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the endpoint. A theoretical proof of the uniform boundedness of velocity and position trajectories and the convergence of these to their desired ones is given, together with the convergence of force errors. Computer simulation remits by using a 3-DOF manipulator are presented to demonstrate the effectiveness of the proposed method.>

This paper has a twofold objective: 1) to show that, when an adaptive law designed with a Lyapunov function and dependant also of the parametric error is introduced into a variable structure controller the existence of a sliding mode is no longer guaranteed for all t; and 2) a time varying boundary layer modulated by the tracking errors to propose a variable structure robot control with asymptotic stability. It is shown that the high activity on the actuators, typical of variable structure controllers, can be arbitrarily reduced in both adaptive and nonadaptive cases. These controllers give rise to the asymptotic stability and at the same time the sliding mode is guaranteed with very small chattering. A trade off arises in terms of chattering and robustness but the convergence of tracking errors is always assured. Computer simulations show the performance of the proposed controllers with comparison of two other algorithms.>

This paper investigates behavior of a single mobile robot which is navigated by an "iterated forecast and planning" scheme in an environment where multiple obstacles are moving around. This navigation scheme, which was previously proposed by the authors, searches a feasible path for a robot in (x,y,t) space by a heuristic method. The movement of each obstacle is then forecasted under the assumption that it moves with a piecewise constant velocity. The planning algorithm is iterated frequently to accommodate the actual changes in the obstacles' velocity. This paper examines various kinds of the behavior obtained by different experimental conditions in the computer simulations.>

When multiple manipulators are going to manipulate one target object coordinately, it is necessary to control both the motion of the object and the forces exerted at the endpoints of manipulators. It is important particularly to control the so-called internal force which does not affect the motion of the object. In this paper, a model-based adaptive control method for such coordinated control of multiple manipulators is proposed. The proposed method is a natural extension of the adaptive control scheme for geometrically endpoint constrained manipulators. It is proved theoretically that both the desired position trajectory of the object and the internal force converge to the desired ones respectively. The effectiveness of the proposed method is demonstrated by computer simulation using two planar 3-DOF manipulators.>

Estimation of the position based on a conventional dead-reckoning for a mobile robot intrinsically contains cumulative errors. In relation to this, large nonzero probability of its error distribution is occasionally found even outside the workspace, because the dead-reckoning takes no account of the unreachability outside the workspace. This paper presents a new fusion algorithm for dead-reckoning, in which both the dead-reckoned position and the knowledge of the closed workspace are fused by means of Bayesian inference. This algorithm produces zero probability outside the workspace theoretically. Through a computer simulation, the effectiveness of the proposed algorithm is confirmed.>

The tangent graph is a powerful graphic data structure for the path-planning of a mobile robot among obstacles because it has fewer edges than the widely used visibility graph. This paper proposes an efficient algorithm for computing the tangent graph of a set of polygonal obstacles, The algorithm at first detects common tangents of obstacle boundaries by moving-line processing that cooperatively moves a straight segment on the boundaries, and then checks for intersections among detected tangents and the obstacles both by a sequential approach and a sweep-line technique. It is proved that the moving-line processing takes O(MN) and O([M+R]N) computation time in the best and worst cases, respectively, where N expresses the number of obstacle vertices, and M denotes the number of convex segment chains of the obstacle boundaries, and R is a parameter representing the complexity of the boundaries.>

Passive binocular stereo tends to produce stereo correspondence errors and require much computation. A method which overcomes these drawbacks by moving the stereo camera actively is presented. The method utilizes a motion parallax acquired by a monocular motion stereo to restrict the search range of binocular disparity. Using only the uniqueness of disparity makes it possible to find reliable binocular disparity and occlusion very efficiently. Experimental results with complicated scenes are presented to demonstrate the effectiveness of this method.>

A learning control scheme for class of robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two different signals updated separately at every trial by different laws. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the manipulator endpoint. A theoretical proof of the uniform boundedness of velocity and position trajectory errors and the convergence of their desired trajectories is given. In addition, a theoretical proof of the convergence of force errors when velocity and position trajectories are in a neighborhood of the desired ones is presented. Computer simulation results for a 3 DOF manipulator are given.

This paper addresses the problem of navigation for a single mobile robot in an environment of multiple moving obstacles. It presents a near learning algorithm extension to a previously reported (nonlearning) planner. This combined planning system performs the two complementary functions: (1) it derives most feasible velocity of the robot under forecasts for the movements of obstacles in space-time, and (2) it stores obstacle state and feasible velocity values for future reference. Motion planning to derive a feasible velocity of the robot and movement according to the velocity are iterated at regular short intervals. If the robot is confronted with the same situation that is memorized before, the memorized velocity of the robot corresponding to the situation will be used. The algorithm's performance is demonstrated with data from computer simulations.

A theory of training-based learning control is developed for a class of robotic tasks under geometric endpoint constraints. An algorithm for updating the control input which makes the next input consist of the previous input plus modified terms of previous velocity and force errors at the robot endpoint constrained on a surface is proposed. Simulation results are presented to demonstrate the convergence of position and force tracking to a desired path with force specified on the surface. It is shown that the robot dynamics satisfies the passivity condition regarding the joint torque input vector versus the joint velocity vector, even in the case of geometric constraints. A theoretical proof of the convergence of position and force errors is given. In the proof, a relaxed concept of passivity of error dynamics of robot arms plays a crucial role.>

This paper describes an efficient algorithm of path planning for an internal gas pipe inspection robot. Since the purpose of this robot is to move in pipes to detect injures and scratches, the path should cover all pipes in a given map. The problem to find a path along which the robot can investigate all pipes at least so that the total distance is minimum is called 'Chinese Postman Problem'. Usually this problem is NP-complete. If all values of pipes are non-negative, the numerical programming is applied for finding the optimal path. However this is still too complicated to apply for searching the path. Motivated by this motivation, a method to find the optimal path based on the A* algorithm is proposed, which is treated more easily than the numerical programming. Finally some numerical simulation results are presented.

A class of learning control algorithms with a forgetting factor 1> alpha >0 and without differentiation of velocity signals is proposed, which updates the input by u/sub k+1/=(1- alpha ) u/sub k/+ alpha u/sub 0/+ Phi e/sub k/, where u/sub k/ and e/sub k/ stand for command input and velocity error at kth exercise, respectively. The robustness of this learning control with respect to reinitialization errors, fluctuation of dynamics, and measurement noise is studied. It is shown that the exponential passivity of displacement robot dynamics plays a crucial role in the uniform boundedness of transient behaviors and the convergence in the progress of learning. A method called selective learning, which updates u/sub 0/ in the long-term memory by selecting the best command among the past several trials, is proposed. It is claimed that this method accelerates the speed of convergence.>

A tangent graph (T-graph) for path planning of mobile robots among polygonal and curved obstacles is proposed. In the T-graph, the nodes correspond to tangent points on obstacle boundaries and the edges represent collision-free common tangents of the obstacles or convex boundary segments between the tangent points. The T-graph requires O(K/sup 2/) memory, where K denotes number of convex segments of obstacle boundaries. Further, to avoid recomputation of configuration spaces of circular robots in a polygonal environment when their radii has changed, an extended tangent graph (ETG) is developed. The ETG does not depend on obstacles in configuration spaces of robots but on obstacles in real workspace. Consequently, it is possible to plan a path flexibly according to different robots' sizes and safety distances by the ETG.>

It is claimed that 'robotics' is not a test bed for AI but should involve a research frontier relating to the physics underlying human activities such as perception, remembering, planning, practice, and skill. In addition to traditional AI and neural network approaches, other domains that can account for any aspect of human intellectual behavior must be exploited, and tools that actualize real implementation of intelligence in machines need to be devised. A practice-based learning domain for skill refinement and a design tool for a signal-based structured information base for skill acquisition are presented.>

Presents an approach for optimizing the joint torques on the minimum-time trajectory in the criterion of minimum torque change. The torque change a summation of absolute values of differences between the torques at two discrete points, is a nonlinear function. To avoid the authors nonlinear optimization, the authors introduce a linear objective function which gives the same effect. On the basis of the linear objective function and dynamics of the system which constrains the joint torques and the internal forces between the manipulators and the object linearly, the linear programming approach is used to optimize the joint torques.>

The authors propose an active algorithm to detect binocular disparities without using additional constraints. It is based on the motion parallax obtained by the moving monocular camera. The search range of binocular disparity is restricted based on the monocular motion parallax. The condition to find binocular disparities is only the uniqueness of disparity. Experimental results with complicated scene are presented to demonstrate the effectiveness of this method.>

A problem of task planning of a robot can result in, another problem of finding a path that connects some given initial point and final point. The article suggests prospect of a 'wave front method' that can find one of the shortest paths in a computation time being proportional to the length of the shortest paths. This wave front method is based on an analogy of the Huygens' principle known in physics, and can be extended into the time dimension.>

A class of simple learning control algorithms having a forgetting factor but not making use of the derivative of velocity signals for motion control of robot manipulators is proposed, and its convergence property is discussed. The robustness of such a learning control scheme with respect to initialization errors, disturbances, and measurement noise is studied. It is proved that motion trajectories converge to a neighborhood of the desired trajectory and eventually remain in it. Relationships of the size of attraction neighborhoods to the magnitudes of initialization errors and other disturbances are obtained, suggesting a rule for selection of the forgetting factor in the progress of learning.>

A method is proposed to control the tip of a flexible multilink manipulator. The external sensory feedback controller can be easily constructed in task-oriented coordinates (for example Cartesian coordinates) without complex calculation for inverse kinematics or inverse dynamics. To realize the control method, a reduced-order approximate Jacobian which is formed by the motion of both rigid and flexible modes of the manipulator is introduced. The effectiveness of the proposed control method based on the approximate Jacobian is proved by using L/sub 2/ stability theory. The practical usefulness of the method is demonstrated by some experimental results for a flexible two-link manipulator.>

When multiple mobile robots are working in the same environment, planning of collision-free coordinated motion is necessary; here, an algorithm for planning such a motion of two mobile robots, no matter how crude the constraints of obstacles are, is proposed. The situation is modeled as a Petri net, which is considered as a useful model for describing and analyzing a system in which it is possible for some events to occur concurrently but there are constraints on the concurrence. In the Petri net, all motion constraints of robots in their paths are arranged as its firing rules, and hence collision-free coordination between the robots can be easily planning by manipulation of the firing rules. The algorithm always finds a collision-free coordinated path of two robots if there actually exists such a path in the environment. Moreover, because the algorithm does not use any knowledge of movement of the robots, precise time-varying trajectory control is not required and realization of the coordination is easy. The algorithm works efficiently even in a complex environment, indebted to the generic properties of geographical quadtree modeling for the environment. The usefulness of the algorithm is shown by several simulations.>

The positioning control problem of the end tips of space manipulators which have no fixed bases is investigated. According to the momentum-conservation law, the system is represented by a nonholonomic model. Thus, the conventional control method for industrial robots, based on a local feedback at each joint, is not applicable when the end tip must be positioned at a floating target. For this problem, the sensory feedback control scheme is based on the artificial potential defined in the sensor coordinate frame. The generalized Jacobian plays an important role in determining the control torque of each joint from the data of the external sensors. This scheme is simple, and the stability of the system is strictly assured. The approximate Jacobian, which needs less computation and less parameter identification, is shown to work well.>

A motion-planning algorithm is proposed which fulfils its function fast even if shapes of the robot and its obstacles are complicated. Considering the global obstacle allocation in the robot workspace, the proposed algorithm selects intermediate positions where the mobile robot should pass from a start position to a goal position. Using a systematic motion generation method based on the closest points between the robot and its obstacles, the algorithm generated collision-free robot motions to joint the intermediate positions successively. The algorithm runs on the quadtree representation, obtained from fast conversion of a real image taken through a camera on the ceiling of the workspace. The algorithm can generate collision-free motions while following a change of obstacle allocation. In a comparison with several motion-planning algorithms, it is shown that the proposed algorithm generates fast collision-free robot motions.>

A fast algorithm is presented to measure obstacle shape in the workspace and to register the shape to the octree that is suitable for path and motion planning of the robot. The octree is constructed hierarchically by a set of cubic regions in the workspace, and its data structure is suitable for detecting interference between a moving robot and its obstacles and searching for a free region in the robot path planning.>

A theorem is proposed and used to prove that a robot can track a desired trajectory described by time functions with required arbitrary accuracy, if a linear PD (proportional-derivative) feedback loop is provided at each joint of the robot and the velocity feedback gain can be set sufficiently large. It is shown how to apply the theorem to real robots in order to realize efficient, high-performance trajectory tracking. The relationship of the control to a linear PID feedback controller previously proposed for positioning is investigated, and a linear PID feedback controller which is capable of not only positioning but also trajectory tracking is obtained.>

A fast, efficient, algorithm that processes polyhedral cones from views of an object to produce an octree representation of the object is proposed. A polyhedral cone defined by both a viewpoint and a polygonal contour of the object in the image for each view always contains the object. Thus the common region of the cones for multiple views is considered to be an approximating one of the object. The octree representation controls all cubic regions in three-dimensional space as a set of nodes with a hierarchical description in positioning, and therefore is suitable for registration of the common region. The computational complexity of the algorithm is directly proportional to the common region surface area and exponentially proportional to the finest resolution level of the octree. The calculation time of the algorithm is determined for illustrative examples of three-dimensional objects.>

To make a robot track a given desired motion trajectory, a learning control scheme is proposed which is based on the repeatability of robot motion. In this scheme the robot obtains a desired motion by repeating trials (test motion). A merit of this control scheme is that the input torque pattern that generates the desired motion can be formed without estimating the physical parameters of robot dynamics. In practice, to allow the robot motion to approach the desired one in each trial, the input torque given to the robot at the present trial is modified only by the velocity signal of the real robot motion at the previous trial and the desired one. The convergence to the desired motion is theoretically proved for a linear time-varying mechanical system, which is an approximate representation of nonlinear robot dynamics in the vicinity of the desired motion. The effectiveness of this control scheme is demonstrated through actual experiments in which a revolute-type manipulator with three degrees of freedom is used, and the desired motion trajectory is given not only in terms of joint-angle coordinates but also in terms of task-oriented coordinates.>

A new cooperative motion control scheme for multiple robot arms or fingers is proposed, which is derived from the analogy of a bilateral control for teleoperation of a system of master and slave robots. A group of some robot arms (or fingers) are moved in cooperation with the master ones. It is shown that a Lyapunov method gives rise to an assurence of the asymptotic.

This paper elucidates the correspondence between the Schur-Cohn (SC) test and the Routh-Hurwitz (RH) test by showing that the latter is obtained as a limit of the former. The argument is developed in a stochastic framework, where stochastic interpretations of the tests are used. The interpretation of the SC test can be derived from its equivalency to the Levinson-Durbin algorithm, while that of the RH test is newly presented here.

A new type of bilateral servo controller for a master-slave robot system (MSRS) is proposed through the stability analysis based on the Liapunov theory. This controller is easily implementable and assures the asymptotic stability in a sense that just after the human operator leaves hold of the joystick, the position of the tip of slave arm asymptotically approaches the position of the joystick of master arm and eventually coincides with it, even if each of master and slave arms has a different structure and complex nonlinear dynamics. It possesses some remarkable functions indispensable for advanced teleoperation systems and consists of three control modes, viz. manual operation mode, force feedback control mode, and hybrid control mode, each of which is selected to use according to the substance of tasks. The performance of teleoperation under each control mode is comparatively examined by referring to experimental results.

A new coding technique called SECT for digitized two-tone or black/white pictures which is particularly applicable to polygonal objects composed of horizontal, vertical, and diagonal lines such as logical circuit patterns, characteristic font patterns, and mechanical drawings is described. While many previously proposed coding techniques attempt to encode the positions of transitive elements in a picture by using codes such as Huffman codes or Wyle codes which are constructed on the basis of picture statistics, selective element coding technique (SECT) focuses its attention on reducing the number of transitive elements in each picture independently of the statistics of the picture ensemble. The relation of the number of selective elements and objects in the picture is discussed. Furthermore the decoding algorithm to reproduce a picture from selective elements and its decodability are described.

A new recursive method is proposed for fitting multivariate ARX models and testing their stability from impulse response and output covariance information. This recursive algorithm, differing from that of Mullis and Roberts, consists of two types of recurrences, one of which is related to updating AR orders and the other to updating MA orders. It offers more computational convenience especially in case of choosing the correct orders of ARX models. It is further shown that "Burg identities" are also proved crucial in deducing some important relations between the resulting polynomial matrices as in the case of the multivariate autoregression. These realtions together with the generalized Rouche 's theorem are applied to testing stability of the fitted ARX models at each step of the recursion.

Channel block codes are shown to have a fine hierarchical structure with respect to the expurgated exponent. Another hierarchy with respect to the random coding exponent is also discussed.

A new proof is presented for the existence of block codes whose error probability under maximum likelihood decoding is bounded asymptotically by the random coding bound universally over all discrete memoryless channels. On the basis of this result, the existence of convolutional codes with universally optimum performance is shown. Furthermore the existence of block codes which attain the expurgated bound universally over all discrete memoryless channels is proved under the use of maximum likelihood decoding.

A general form for the rate-distortion function is presented for the nonstationary Gaussian autoregressive (AR) process and is shown to differ from the well-known form for the asymptotically stationary process in a term corresponding to the log rate of the variance growth if the process has exponentially growing variance.

The long standing conjecture is established that, for a discrete memoryless channel, there exists a linear convolutional code with infinite constraint length such that the\rhoth(\rho \geq 1)moment of the number ofF-hypotheses in the Fano sequential decoding algorithm is bounded, provided that the transmission rateRis less thanE_{0}( \rho,r)/ \rho, wherer(x)is a distribution over the channel input alphabet. A new concept of independence for a finite set of message sequences plays an essential role in averaging a product of likelihood ratios over an ensemble of code sequences in a code tree. A simpler version of the method can be applied to the proof of the conjecture for general tree codes.

Gallager's exponent functionE_{\circ}, (\rho,p)plays a crucial role in the derivation of bounds for coding error probabilities. An iterative algorithm for computing the maximum ofE_{\circ} (\rho,p)over the set of input probability distributions is presented. The algorithm is similar to that of Arimoto and Blahut for computing channel capacity. It is shown that the approximation error is at most inversely proportional to the number of iterations. A similar iterative algorithm for computing the source code reliability-rate function also is presented.

A new lower bound on the probability of decoding error for the case of rates above capacity is presented. It forms a natural companion to Gallager's random coding bound for rates below capacity. The strong converse to the coding theorem follows immediately from the proposed lower bound.

A systematic and iterative method of computing the capacity of arbitrary discrete memoryless channels is presented. The algorithm is very simple and involves only logarithms and exponentials in addition to elementary arithmetical operations. It has also the property of monotonic convergence to the capacity. In general, the approximation error is at least inversely proportional to the number of iterations; in certain circumstances, it is exponentially decreasing. Finally, a few inequalities that give upper and lower bounds on the capacity are derived.