This paper presents adaptive tracking control with a partial regressor for multi-joint robots. The proposed controller can abbreviate the elaborate computations of a regressor matrix, which needs to be wholly calculated in conventional adaptive control. Therefore, this controller can be easily implemented in robotic systems. Even though the proposed controller does not utilize robustification techniques of adaptive control such as deadzone, σ-modification, e-modification, and projection, the trajectory tracking performance and robustness were guaranteed through analysis of the time-averaged tracking error.

This paper presents a novel method for controlling a single-joint robot arm driven by two pneumatic artificial muscles (PAMs). We introduce the concepts of the agonist-antagonist muscle-pairs ratio (A-A ratio) and the agonist-antagonist muscle-pairs activity (A-A activity), and demonstrate that our concepts enable separate linear control of the equilibrium joint angle and joint stiffness. We also discuss our approach in comparison with the equilibrium-point (EP) hypothesis.

This paper presents an electromyographic-based human-machine interface for the agonist-antagonist system with two pairs of pneumatic artificial muscles (PAMs) that replicates the human elbow-joint system. We introduce the novel concepts of agonist-antagonist muscle-pair ratio (A-A ratio) and agonist-antagonist muscle-pair activity (A-A activity) to link the human muscle system to the PAM system, and we propose a linear control method translating the equilibrium point of human muscle system into that of PAM system. The human-robot experiment demonstrates the validity of the proposed method.

The need of solving the problem of multi-degrees of-freedom in motor control is increasing as human-like robots become more popular these days. Muscle synergies hypothesis is a prominent suggestion for this problem. In this paper, we investigated the muscle synergies extracted from human subjects in a producing-force task to explore the mechanism of the human's skillful hand-movement at producing force while manipulating objects. We observed a high correlation estimation of the muscle synergies with the hand force, and derived a role division of muscle synergies in generating the hand force as well. These results give an optimal but simple way to control robots so as to make the robots' movement as skillful as human's. To verify this idea, we proposed "synergy based control" method using the characteristics of human's muscle synergies observed, and tested it on a pneumatic-driven arm robot whose structure assumes the human arm's. The program is in its first stage to control the robot.

The study of decomposition of body movement into motor primitives is evolving in neuroscience. Meanwhile, in robotics, the motor control of human-like musculoskeletal robots is difficult due to redundant degrees of freedom (DOF) of the robot body. The application of the concept of decomposing into units of motor function to robotics is anticipated to render the control of the robot low-dimensional. We try to achieve fewer-DOF control of a human-like musculoskeletal robot by using our knowledge of the units of motor function based on muscular coordination. In this paper, we introduce “the agonist-antagonist muscle pairs (A-A) ratio” and “A-A activity.” These parameters are defined by using electromyographic (EMG) data for describing the coordination between the agonist and antagonist muscles. Human running is decomposed into two units of motor function using Principal Component Analysis (PCA) for these parameters. We propose a new method of modular control of a musculoskeletal leg robot using the extracted patterns of muscle coordination, and we thus find kinematic meanings of the patterns of muscle coordination.

This paper focuses on dynamical role divisions of cooperation interaction between two human subjects in a typical cooperation task, the crank-rotation task. The dynamical role division in which each subject plays a specialized role without conscious understanding is a key issue that brings the efficiency to the performance of the crank-cooperation work. By investigating kinetics and muscle activities, we found out interesting results about the correlation between the muscle activities and the hand motion, thus, suggesting a method to design control of robots which involve in the crank-cooperation task and presumably in other cooperative interaction with humans.

This paper presents feedback control laws for pursuing and catching a fly ball by taking Chapman's hypothesis into the closed-loop system connecting perceptions and actions. Through the analysis of the closed-loop system, we make it clear that the hypothetical trajectory Chapman showed is a special dynamic solution of the closed-loop system. Moreover, using a motion-analyzing technique over a finite time, it is shown that the proposed feedback control laws make it possible to generate a pursuing trajectory automatically that a fly ball can be caught in the right place and at the right time. It is also shown that the pursuing trajectory gets closer to the one Chapman showed as a feedback gain increases. In addition, we compare the proposed feedback control laws with Proportional Navigation (PN) which is the most common navigation technique for tracking a moving target, and demonstrate that the proposed control laws perform better than PN.

This paper discusses skillful role divisions of coordinated motion between two agents in a crank-rotation task. The roles for coordination, called ¿dynamical role division,¿ emerge from dynamic interaction between the agents, through which each agent comes to play a specialized role without conscious understanding. This paper also proposes a novel approach to apply this latent skill in coordinated motions to human-robot coordination, and showing the following advantages of this method: 1) controls of each agent's actions are simplified; 2) task performances are improved in a simple manner.

We propose a method for using surgeon's biological information to evaluate the surgeon's stress in his/her using a surgical assistant system. The surgeon's biological information were saliva and heart rate. The stress was measured by analyzing Cortisol, alpha-amylase, and total protein in saliva, and we analyzed the acceleration plethysmogram variability as the indexes of autonomic nervous activity, those were coefficient of variance of the a-a interval and low-frequency band power spectrum(LF), high-frequency band power spectrum (HF), the ratio of LF to HF (LF/HF). To validate the effectiveness of the proposed method, we conducted a laparoscopic cholecystectomy simulation. In conclusion, we confirmed our method had an ability to objectively evaluate the surgeon's stress during surgery.

This paper describes a hierarchical architecture for rhythmic coordination between robots, which suits a juggling-like ball-passing task involving sensory-motor coordination. The proposed architecture contains two passive-control mechanisms, which include (1) open-loop stability of mechano-physical system and (2) entrainment between mechano-physical system and neuron-like pattern generating system. We demonstrate a stable motion pattern between robots and emphasize that these passive-control mechanisms lead to the emergence of dynamic coordination for the successful task

This paper presents a development of robot system that realizes ball lifting task. The ball lifting is considered as a basic practice in the ball sports such as tennis or soccer. Through this task by the robot, we intend to make clear the process of learning skill to realize the basic task by human. We examine the performance of robot system and demonstrate that the robot enables to perform this task in spite of many difficulties. Our purpose is to develop the intelligent robot system that realizes human skill and we build the basic but significant robot system as a first step

This paper presents a basic examination of skill acquisition of a ball lifting task using a mobile robot with a monocular vision system. Ball lifting is considered as a basic practice in sports such as tennis or soccer. Through this task by the robot, we intend to make clear the process of learning to perform this basic task. We examine the performance of a robot system and demonstrate that the robot performs this task in spite of many difficulties. Our purpose is to develop an intelligent robot system that performs a human skill and we have built a basic but significant robot system as a first step. In this paper, we validate the efficacy of the robot system that we developed

This paper describes the hierarchical architecture for a rhythmic coordination between robots, which suits juggling-like tasks involving sensory-motor coordination. The authors' approach, which is interpreted as a "bidirectional weak coupling" to the environment, does not require either the environmental model or continuously monitoring the environment but can adapt the robots to a change in the environment, owing to the interaction between the robots and the environment at the ball contact. The proposed architecture contains two passive-control mechanisms, the "open-loop stable mechanism" and the "entrainment mechanism," that lead to the emergence of self-organized temporal structure for rhythmic movement. This dynamic pattern in the whole system realizes the stable coordinated motion between robots. The authors demonstrate two motion patterns between two robots passing two balls, and confirm the effectiveness of the approach.

We propose a method of controlling a table tennis robot so as to return the incoming ball to a desired point on the table with specified flight duration. The proposed method consists of the following three input-output maps implemented by means of locally weighted regression: 1) a map for predicting the impact time of the ball hit by the paddle and the ball position and velocity at that moment according to input vectors describing the state of the incoming ball; 2) a map representing a change in ball velocities before and after impact; and 3) a map giving the relation between the ball velocity just after impact and the landing point and time of the returned ball. We also propose a feed-forward control scheme based on iterative learning control to accurately achieve the stroke movement of the paddle as determined by using these maps. Experimental results including rallies with a human opponent are also reported to demonstrate the effectiveness of our approach.

We have proposed a GAG (Gaining Angle of Gaze) strategy for ball catching. The strategy allows single camera configuration and it is robust against camera motion errors. It is superior than previously proposed strategies in the field of experimental psychology in the catchable region and robustness. In this paper, we apply the GAG strategy for tracking and catching a 3D flying target. The GAG strategy is extended for hand-eye robot manipulators and ball catching performance is evaluated by simulations and experiments.

This paper presents an examination of ball lifting task using a mobile robot. We have proposed a trajectory control method for tracking and catching a ball flying in 3D space based on GAG (gaining angle of gaze) strategy. The strategy allows single camera configuration and it is robust against camera motion errors. This simple method was implemented to a mobile robot and we demonstrated the quick motion to catch balls. We consider the extension of this method for the ball lifting task and demonstrate the robot can track and rebound a ball repeatedly as a basic examination of the task.

This paper describes the properties of a set of simple neural network oscillators suited to two robotic tasks. One robotic task is "wall-bouncing", in which the robot repeats the process of hitting balls that rebound from the wall. Another robotic task is "passing a ball", in which two robots repeat the process of passing balls to each other. The motions of the robot (paddle) are controlled by a set of neural oscillators consisting of four weakly coupled Bonhoffer-van der Pol (BVP) oscillators. We demonstrate that rhythmic movement of the paddle emerges as a stable limit cycle generated by the global entertainment between the paddle, the neural system, and the environment, including balls.

We propose a method of controlling the paddle so as to return the ball to a desired point on the table with a specified flight duration. The proposed method consists of the following three input-output maps implemented by means of Locally Weighted Regression (LWR): (1) A map for predicting the impact time of the ball hit by the paddle and the ball position and velocity at that moment according to input vectors describing the state of the incoming ball, (2) A map representing a change in ball velocities before and after impact and (3) A map giving the relation between the ball velocity just after impact and the landing point and time of the returned ball. We also propose a novel control scheme based on iterative learning control to accurately achieve the stroke movement of the paddle as determined by using these maps.

Robotic laparoscope positioners are now expected as assisting devices for solo surgery among endoscopic surgeons. In such robotic systems, the human-machine (surgeon-robot) interface is of paramount importance because it is the means by which the surgeon communicates with and controls the robotic camera assistant. We have designed a novel human-machine interface, called "FAce MOUSe", for controlling the position of a laparoscope. The proposed human interface is an image-based system which tracks the surgeon's facial motions robustly in real time and does not require the use of any body-contact devices, such as head-mounted sensing devices. The surgeon can easily and precisely control the motion of the laparoscope by simply making the appropriate face gesture, without hand or foot switches or voice input. Based on the FAce MOUSe interface, we have developed a new robotic laparoscope positioning system for solo surgery. Our system allows nonintrusive, nonverbal, hands off and feet off laparoscope operations, which seem more convenient for the surgeon. To evaluate the performance of the proposed system and its applicability in clinical use, we set up an in vivo experiment, in which the surgeon used the system to perform a laparoscopic cholecystectomy on a pig.

This paper describes how a table tennis robot with a flat paddle coordinates its movement in order to achieve efficient strokes for any given ball. We propose a method of generating stroke movement based on virtual targets that means the point at which the ball should be struck and the paddle velocity just before hitting the ball. These targets are predicted using inputs-outputs maps implemented efficiently by means of a k-d tree (k dimensional tree). The paddle approaches these targets by using a visual feedback control scheme similar to the mirror law proposed by Koditschek. The results of the implementation are also given to show the effectiveness of the proposed method.

In this paper, we examine the human ball catching strategy called LOT (linear optical trajectory) that had been proposed in the field of human behavior through an autonomous mobile robot. LOT is a model of the motion strategy that explains the rationality of the running path of outfielder in catching the balls. However, this model does not provide concrete shape about how fielders decide the running direction and velocity according to the ever-changing position of balls. We consider the ball catching task by an autonomous mobile robot and derive a visual feedback control method based on LOT. We also demonstrate that the proposed method enables the mobile robot to catch the balls that fly in three-dimensional space only by using the monocular vision system.

In this paper, we consider ”wall-bouncing ” task in which we repeat the process of hitting the ball that rebounds from the wall and the table as a typical example of dexterous batting tasks. Through the stability analysis of a discrete system, we reveal that required dexterity varies depending on the location of equilibrium points, that is, the shape of the ball trajectory. A feedback control scheme for the paddle at the moment of impact is also proposed to continue ”wall-bouncing ” task under disturbances.

In this paper, we propose a ball tracking and catching strategy called GAG that enables a mobile robot to track and catch a ball flying in three-dimensional space. The GAG strategy does not require the information of the ball's position in three-dimensional space, but instead requires only the angle of elevation of gaze captured as two-dimensional visual information. We also derived a visual feedback control scheme based on GAG and demonstrated that the proposed scheme enables the robot to track and catch a ball flying in three-dimensional space by using only a monocular vision system.

Close circumnavigation is an important function indispensable for servicing satellites. We discuss the bielliptic flyaround maneuver by impulsive thrust control for a small and low cost servicing satellite flying around a target satellite. An optimal feedback control scheme for the thrust is proposed to maintain this trajectory in the presence of disturbances. The extended Kalman filter is employed to estimate state variables which are not available for measurement. Simulation results that verify the trajectory keeping capability of the proposed thrust control are presented.

Segmentation of an articulated object is one of the fundamental techniques of computer vision, and it can be applied to human body structure analysis and motion measurement. In this report, we propose a method that generates the contour model of each link element of an articulated object by using the hierarchical part decomposition (HPD) method of body contour. The HPD can divide the body region into characteristic elements without any model of link parameters or shape based on the distribution of high-curvature points of the object's contour in the video sequence. Although the HPD only works under the restricted situation, the proposed method can also handle more general body motion by using the shape model of human body generated from the decomposed result. Experiment results of measuring the kip motion under real environment show that the proposed method can divide the body into characteristic elements and obtain postures of the body from the video sequence.

The kip is a fundamental gymnastic movement performed on a variety of gymnastic apparatus at all levels of competition. We analyze the kip with a reduced model approach consisting of a pendulum and 3-link system. Our simulations have shown that a variable length pendulum is sufficient for modeling the dynamics of an expert gymnast's center-of-mass without considering the complex parameters of the 3-link system. The optimized pendulum model can generate kip pattern variations which are evaluated based on the center-of-mass and 3-link system's total energy. A comparison of the performances of robot, simulator and human expert identifies (1) body arching during the forward swing, (2) a quick piking movement, and (3) grip adjustment during the backward swing as characteristics common to success. These results suggest a guideline for performing the kip successfully.

We address the acquisition of 3D structure around free spaces needed for vehicle navigation using an active stereo vision system that can fixate on any 3D point of the scene. Our method is based on the hypothesis that there are larger and deeper free spaces in the same direction in which there are larger and farther visible objects. We first estimate the direction of promising free spaces by fixating on the infinity point. Then we acquire the partial 3D structure around the free spaces by successively fixating on several 3D points in the estimated direction. We finally present experimental results of the proposed method applied to actual scenes.

In order for a robotic system autonomously to capture an unknown rigid body rotating freely in zero gravity, it is necessary for it to detect the object's motion. This paper presents a method for estimating object motion based on position information concerning a point located on the object's surface. We assume that the object's motion is described by the superposition of the rotations with constant angular velocities. When position sensors, etc., can obtain three dimensional position information about a point on the object, we can estimate parameters describing its motion. In other words, using this method we can calculate each frequency and direction of the axes of rotation by applying a Fourier transform to a time sequence of the point's three dimensional position.

The authors present a system for coaching novice gymnasts in performing the kip based on detailed body information. They study the kip using a pendulum and kinematically redundant 3-link model. Their simulations have shown that a variable length pendulum model is sufficient for modeling the dynamics of an expert gymnast's center-of-mass without considering the complex parameters of the 3-link model. The authors optimize the dynamics equations of the pendulum using boundary conditions obtained from an expert gymnast's center-of-mass trajectory. By varying these conditions they discover target and timing regions of success and other finds of basic coaching information. The authors input this information to the 3-link model to obtain detailed coaching information such as center-of-mass trajectories matched to the user's body characteristics and corresponding torque data. This system improves on current coaching techniques for the kip.

Most work on compliance or force control schemes in doing precision assembly has been insufficient to provide satisfactory performance, and therefore applications to manufacturing have been few. The remote centre compliance (RCC) device is the most famous of the passively compliant devices and has been already used in some operations. However, it is well known that the RCC has some limitations in flexible precision assembly automation: its characteristics (such as the position of remote compliance center and compliance at the point) are not variable. The device has not in fact been improved since its invention 20 years ago. To confer more flexibility to a robotic assembly line, we developed a cost-effective, compact, and lightweight variable RCC (VRCC). The invention of a simple but unique mechanism enabled the fabrication of the variable RCC. The device eliminates the need for an additional complex electrical control system or theory to cope with variation. Moreover, we have verified that a single VRCC duplicates the performance of a wide range of commercially available fixed-type RCCs.

In the drive towards miniaturization in manufacturing, accuracy in positioning minute objects by camera is vital. For visual servoing, the rapid and robust detections of features in camera images is also essential to production line efficiency. Template matching provides flexibility in achieving this, often lacked by other methods, because it avoids the need to set object-specific parameters. Unfortunately, standard methods of template matching require much calculation, especially for detecting feature rotation. The delay this causes means that for many applications template matching provides too slow a source of visual feedback. As an alternative, we propose a new method of detecting the translation and rotation of a feature from coarse optical flow, we and apply it to visual servoing. Coarse optical flow is derived from the difference in intensity between a region of the initial and current image and their pixel-by-pixel intensity gradients. Unlike template matching, our method can detect large rotations with relatively little calculation. Image resolution is then adjusted from coarse to fine. Subpixel accuracy results in a 100 fold improvement in precision (by area). We show experimental results for precise planar positioning.

The ability to acquire 3D information about passable free spaces and objects around them is essential for mobile robot navigation in unknown scene. We present a new method of selective reconstruction of a 3D scene around free spaces with an active stereo vision system. Our method is based on the following observation: The larger and deeper passable free spaces may exist in the direction to which the farther 3D objects exist because the set of line of sights connecting the viewpoint with visible object surfaces form free spaces. First, we selectively reconstruct 3D objects far from the viewpoint using parallel stereo. We call them "distant objects". Then, as the direction, to which larger and deeper free spaces may exist, we select the direction of the "distant object" whose projection onto the image plane is larger. Finally, we efficiently reconstruct 3D objects in the selected direction using an active stereo vision system that can fixate any 3D point of the scene. Experimental results of the proposed method applied to the actual scene are also presented.

The mechanical behaviour of RCC devices using elastomer shear pads is influenced by the number of rubber-metal layers, thickness and radius of the rubber and metal, and axial and lateral stiffness of the rubber (elastomer). These parameters have to be considered for a complete analysis or design of the RCCs. We present a useful method of analyzing the behaviour of RCC based on the basic knowledge of the mechanics of materials. This makes it easy to design a new RCC for a specific purpose. Simulation and experimental results are also presented.

We propose a simple visual servoing scheme based on the use of binocular visual space. When we use a hand-eye system which has a similar kinematic structure to a human being, we can approximate the transformation from a binocular visual space to a joint space of the manipulator as a linear time-invariant mapping. This relationship makes it possible to generate joint velocities from image observations using a constant linear mapping. This scheme is robust to calibration error, especially to camera turning, because it uses neither camera angles nor joint angles. Some experimental results are also shown to demonstrate the positioning precision remained unchanged despite the calibration error.

This paper describes a new binocular tracking method using log polar mapping (LPM) which approximately represents the mapping of the retina into the visual cortex in primate vision. Using LPM makes it possible not only to obtain both a high central resolution and a wide field of view, but also to significantly reduce the processing of image data. In this paper, LPM is performed in software by the lookup table method. Our tracking method utilizes a zero disparity filter (ZDF) for extracting the target object and virtual horopter method for estimating binocular disparities, respectively. The performance of both target extraction and disparity estimation is improved in comparison with the conventional methods, by using LPM. Some experimental results are also shown to demonstrate the effectiveness of the proposed method.

We propose a method to detect polyhedral object surfaces by using "occlusion information" from active binocular stereo. Occlusion information announces whether each feature point in the scene is visible or not from each viewpoint from which an image has been taken; this is a useful geometric cue to suppose the existence of object surfaces. It is very difficult for conventional stereo methods to obtain this information. Two complementary algorithms based on occlusion information decide whether or not there is a surface between each hypothetical 3D segment called "arc" which corresponds two successive image feature points along an epipolar line. The final surface description of the proposed method is a set of these arcs classified into three states: 1) there is a surface, 2) there is no surface, 3) it is unknown whether or not there is a surface. Our method can reliably detect polyhedral object surfaces that are visible and occlude other objects in the scene. Experimental results of the proposed method applied to the actual scene are also presented.

This paper presents a method to estimate and to predict general three-dimensional motions of an unknown rigid body under no external forces and moments using visual information, which is applicable to autonomous space robotic missions. Four parameters of dynamics and a reference coordinate frame to describe the motion are computed based on the Euler's equation of motion from a sequence of angular velocity vectors extracted from difference of images. Through computer simulations for various kinds of motions the effectiveness and limitation of this method are discussed.>

This paper discusses robot motion planning in dynamic environments, and especially generation of a motion pattern of a manipulator to achieve a task under restrictions of time and space. We focus on a game of "kendama" (Japanese ball-in-cup game) that requires such a task. We do not intend to make robots mimic the actions of human being, but we discuss robots acting based on outside sensory feedback signals. In the "kendama" task, the robot learns physical properties through trials, because no a priori knowledge of both the mass of the ball and the length of the string is available. This paper proposes a method to generate the motion pattern of the manipulator with a force sensing device attached to the end tip to perform the "kendama" task. Experimental results obtained using a two DOF direct-drive manipulator are also shown.>

Although visual servoing is suitable for control of a mobile robot based on a single on-board camera image, wheeled vehicles have a non-holonomic property that the degree of freedom of the input is less than that of the configuration. Thus the visual servoing can not be directly applied to a camera system fixed on the vehicle. Furthermore there is no smooth and stable state feedback law for non-holonomic vehicles. To overcome these difficulties, the authors consider another situation, that the pan angle of camera is actuated and used as an input of visual servoing. The desired state is given as not a constant value but a trajectory represented in terms of image sequences of a gazed object. Based on this idea, the authors propose a method of realizing "teaching and playback" for a non-holonomic mobile robot. In the teaching mode, the robot tracks a landmark in the environment by a single on-board camera and stores its image sequences while running commanded by a human operator. In the playback mode, the robot tracks the desired trajectory with velocity commands for the vehicle and the pan angle for the camera determined from the stored and current images. The authors discuss the convergence to a desired state and moreover point out an additional feedback term, of a deviation of the pan angle works to improve the tracking performance. The effectiveness of the proposed method is shown by illustrating computer simulation results.>

We describe our approach to the robotic Hanetsuki task (Japanese badminton), that is, to return the incoming shuttlecock to the human's side with a racket. A learning method using a novel database is proposed to compensate for the insufficiency in a model-based approach. Experimental results obtained with the developed Hanetsuki robot are presented.>

We present a binocular gaze holding method of an object which is moving in the complicated scene with the Active Stereo Vision System. The system uses a binocular vision robot, which can simulate the human eye movements. Holding gaze on a target object with the controlled cameras keeps the target's stereo disparity small, and simplifies the visual processing to locate the target for pursuit control. The novel point of our tracking method is the disparity-based segmentation method of the target object. The method utilizes a zero disparity filter (ZDF) and correlation to separate the target object with small disparity from distracting background. Furthermore, using the correlation method to estimate stereo disparity makes it possible to fixate on a surface of the target object. We show the experimental results with the complicated scene to demonstrate the effectiveness of the proposed method.>

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 new method of visual servoing with the stereo vision to control the position and orientation of the manipulator with respect to an object is presented. Conventional control methods by visual servoing use a monocular camera and have several problems. For example, either shape information or desired distance of the target object from the camera must be given. Furthermore, a stability problem arises unless the initial positional error of features in the image is sufficiently small. These problems are caused by the image Jacobian matrix, that is, its provisional value at a desired position is used instead of the correct one. By using stereo vision, the image Jacobian matrix can be calculated correctly at any position, so neither shape information nor desired distance of the target object is required. Also, stability is assured even if the initial error is not small. Both simulation and experimental results are presented to demonstrate the effectiveness of this method.

We propose an algorithm that esti- mates tumbling motions of unknown rigid bodies un- der no gravitation based on visual information. Ge- ometric analysis enables determining four parameters of dynamics and reference coordinates from the se- quence of angular velocity vectors which are extracted from difference of images. Computer simulations con- sidering quantization errors show the effectiveness of algorithm.

Proposes a stable control rule to find a reasonable target linear and rotational velocities ( nu , omega )/sup t/. The stability of the rule is proved through the use of a Lyapunov function. The rule contains three parameters, K/sub x/, K/sub y/ and K/sub theta /. Although any set of positive parameters makes the system stable, a condition on the parameters for the system being critically damped for a small disturbance is obtained through linearizing the system's differential equation. This method was successfully implemented on the autonomous mobile robot Yamabico-11. Experimental results obtained turn out to be close to the results with the velocity/acceleration limiter.>

Discusses a new type of learning control scheme for a class of discrete-time nonlinear system. The algorithm of the proposed learning control utilizes a local linearization technique by using a discrete Fourier transform (DFT) to design a learning operator and the numerical function iterative techniques. The secant method is used, which can find the best learning operator by itself at each learning step, in other words, at each calculation step of iteration. This proposed learning algorithm has been extensively tested by simulation on the computer.>

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.>

Discusses robot motion planning in dynamic environment, that is, generation of a motion pattern to attain a working purpose. The authors examine the relation between this redundancy and the dynamic dexterity of robots. Especially the authors focus on 'juggling' as a typical example of such a working purpose(task). To juggle several balls in the gravitational field, the motion of the robot is determined considering the dynamic property of balls. It is shown that a parametric representation of the robot motion makes it easy to generate an appropriate trajectory of the end-effector for 'juggling task'.>

A stable tracking control rule is proposed for nonholonomic vehicles. The stability of the rule is proved through the use of a Liapunov function. Inputs to the vehicle are a reference posture (x/sub r/, y/sub r/, theta /sub r/)/sup t/ and reference velocities ( nu /sub r/, omega /sub r/)/sup t/. The major objective of this study is to propose a control rule to find reasonable target linear and rotational velocities ( nu , omega )/sup t/. Linearizing the system's differential equation is useful for deciding parameters for critical dumping for a small disturbance. In order to avoid slippage, a velocity/acceleration limitation scheme is introduced. Several simulation results are presented with or without the velocity/acceleration limiter. The control rule and limiting method proposed are robot independent and hence can be applied to various kinds of mobile robots with a dead reckoning ability. This method was implemented on the autonomous mobile robot Yamabico-11. Experimental results obtained are close to the results with the velocity/acceleration limiter.>

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.>

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 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.>

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.

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.