This paper proposes a gonio-spectral imaging system for measuring light reflection on an object surface by using two robot arms, a multi-band lighting system, and a monochrome digital camera. It allows four degrees of freedom in incident and viewing angles necessary for full parametrization of a reflection model function. Spectral images captured for various incident and viewing angles are warped as if they were all captured from the same viewing direction. The intensity of reflected light is thus recorded in a normalized image form for any incident and viewing directions. The normalized images are used to estimate reflection model parameters at each surface point. To ensure point-wise reflection modeling, a calibration method is also proposed based on a geometrical model of the robot arms and camera. The proposed system can deal with objects with surface texture. Experiments are done on system calibration, reflection model, and spectral estimation. The results using colored objects show the feasibility of the proposed imaging system.

The purpose of the study is to obtain the automatic and optimal matching between a motion-measurement device such as a data glove and an output device such as a dexterous robot hand, where there are many differences in the numbers of degree of freedom, sensor and actuator positions, and data format, by means of motion imitation by the humans. Through the algorithm proposed here, a system engineer or user need no labor of determining the values of gains and parameters to be used. In the system, a subject with data glove imitated the same motion with a dexterous robot hand which was moving according to a certain mathematical function. Autoregressive models were adapted to the matching, where each joint angle in the robot and data glove data of the human were used as object and explanatory variables respectively. The partial regression coefficients were estimated by means of singular value decomposition with a system-noise reduction algorithm utilizing statistical properties. The experimental results showed that the robot hand was controlled with high accuracy with small delay, suggesting that the method proposed in this study is proper and easy way and is adaptive to many other systems between a pair of motion-measurement device and output device.

This paper presents a new type of wearable teleoperation system that can be applied to the control of a humanoid robot. The proposed system has self-contained computing hardware with a stereo head-mounted display, a microphone, a set of headphones, and a wireless LAN. It also has a mechanism that tracks arm and head motion by using several types of sensors that detect the motion data of an operator, along with a simple force reflection mechanism that uses vibration motors at appropriate joints. For remote tasks, we use intelligent self-sensory feedback and autonomous behavior, such as automatic grasping and obstacle avoidance in a slave robot, and we feed the information back to an operator through a multimodal communication channel. Through this teleoperation system, we successfully demonstrate several teleoperative tasks, including object manipulation and mobile platform control of a humanoid robot.

Eye contact is an effective means of controlling human communication, such as in starting communication. It seems that we can make eye contact if we simply look at each other. However, this alone does not establish eye contact. Both parties also need to be aware of being watched by the other. We propose a method of bidirectional eye contact satisfying these conditions for human-robot communication. When a human wants to start communication with a robot, he/she watches the robot. If it finds a human looking at it, the robot turns to him/her, changing its facial expressions to let him/her know its awareness of his/her gaze. When the robot wants to initiate communication with a particular person, it moves its body and face toward him/her and changes its facial expressions to make the person notice its gaze. We show several experimental results to prove the effectiveness of this method. Moreover, we present a robot that can recognize hand gestures after making eye contact with the human to show the usefulness of eye contact as a means of controlling communication.

We are developing a helper robot that carries out tasks ordered by the user through speech. The robot needs a vision system to recognize the objects appearing in the orders. It is, however, difficult to realize vision systems that can work in various conditions. Thus, we have proposed to use the human user's assistance through speech. When the vision system cannot achieve a task, the robot makes a speech to the user so that the natural response by the user can give helpful information for its vision system. Our previous system assumes that it can segment images without failure. However, if there are occluded objects and/or objects composed of multicolor parts, segmentation failures cannot be avoided. This paper presents an extended system that tries to recover from segmentation failures using photometric invariance. If the system is not sure about segmentation results, the system asks the user by appropriate expressions depending on the invariant values. Experimental results show the usefulness of the system.

This paper aims at challenging Bernstein's problem called the "Degrees-of-Freedom problem," which remains unsolved from both the physiological and robotics viewpoints. More than a half century ago A.N. Bernstein observed that "dexterity" residing in human limb motion emerges from accumulated involvement of multi-joint movements in surplus DOF. It is also said in robotics that redundancy of DOFs in robot mechanisms may contribute to enhancement of dexterity and versatility. However, kinematic redundancy incurs a problem of ill-posedness of inverse kinematics from task-description space to joint space. In the history of robotics research such ill-posedness problem of inverse-kinematics has not yet been attacked directly but circumvented by introducing an artificial performance index and determining uniquely an inverse kinematics solution by minimizing it. Instead of it, this paper introduces two novel concepts named "stability on a manifold" and "transferability to a submanifold" in treating the case of human multi-joint movements of reaching and shows that a sensory feedback from task space to joint space together with a set of adequate dampings enables any solution to the overall closed-loop dynamics to converge naturally and coordinately to a lower-dimensional manifold describing a set of joint states fulfilling a given motion task. This means that, without considering any type of inverse kinematics, the reaching task can be accomplished by a sensory feedback with adequate choice of a stiffness parameter and damping coefficients. It is also shown that these novel concepts can cope with annoying characteristics called "variability" of redundant joint motions seen typically in human skilled reaching. Finally, it is pointed out that the proposed control signals can be generated in a feedforward manner in case of human limb movements by referring to mechano-chemical characteristics of activation of muscles. Based on this observation, generation of human skilled movements of reaching can be interpreted in terms of the proposed "Virtual-Spring" hypothesis instead of the traditional "Equilibrium-Point" hypothesis.

This paper is devoted to the problem of force sensorless disturbance rejection in robot manipulators. In the proposed approach, the control system uses position sensor signals and estimated values of external forces, instead of force sensor signals. The estimation process is performed via an adaptive force estimator. Then the estimated force vector is utilized to compensate for the force disturbance effect in order to achieve a better trajectory tracking performance. The force estimation is carried out directly using no environment model. Asymptotical stability of the proposed control system is analyzed by the invariant set and Lyapunov direct method establishing an appropriate theorem. Finally, the performance of the proposed control system is verified using numerical simulation.

This paper proposes near-field sound-source localization based on crosscorrelation of a signed binary code. The signed binary code eliminates multibit signal processing for simpler implementation. Explicit formulae with near-field assumption are derived for a two microphone scenario and extended to a three microphone case with front-rear discrimination. Adaptive threshold for enabling and disabling source localization is developed for robustness in noisy environment. The proposed sound-source localization algorithm is implemented on a fixed-point DSP. Evaluation results in a robot scenario demonstrate that near-field assumption and front-rear discrimination provides almost 40% improvement in DOA estimation. A correct detection rate of 85% is obtained by a robot in a home environment.

This paper proposes an adaptive noise canceller (ANC) with low signal-distortion for human-robot communication. The proposed ANC has two sets of adaptive filters for noise and crosstalk; namely, main filters (MFs) and subfilters (SFs) connected in parallel thereto. To reduce signal-distortion in the output, the stepsizes for coefficient adaptation in the MFs are controlled according to estimated signal-to-noise ratios (SNRs) of the input signals. This SNR estimation is carried out using SF output signals. The stepsizes in the SFs are determined based on the ratio of the primary and the reference input signals to cope with a wider range of SNRs. This ratio is used as a rough estimate of the input signal SNR at the primary input. Computer simulation results using TV sound and human voice recorded in a carpeted room show that the proposed ANC reduces both residual noise and signal-distortion by as much as 20 dB compared to the conventional ANC. Evaluation in speech recognition with this ANC reveals that with a realistic TV sound level, as good recognition rate as in the noise-free condition is achieved.

We propose a modified Hidden Markov Model (HMM) with a view to improve gesture recognition using a moving camera. The conventional HMM is formulated so as to deal with only one feature candidate per frame. However, for a mobile robot, the background and the lighting conditions are always changing, and the feature extraction problem becomes difficult. It is almost impossible to extract a reliable feature vector under such conditions. In this paper, we define a new gesture recognition framework in which multiple candidates of feature vectors are generated with confidence measures and the HMM is extended to deal with these multiple feature vectors. Experimental results comparing the proposed system with feature vectors based on DCT and the method of selecting only one candidate feature point verifies the effectiveness of the proposed technique.

Intelligent robot control systems based on multiprocessors, sensors, and actuators require a flexible network for communicating various types of real-time data (e.g. sensing data, interrupt signals). Furthermore, serial data transfer implemented using a few wiring lines is also required. Therefore, a CDMA serial communication interface with a new two-step synchronization technique is proposed to counter these problems. The transmitter and receiver fabricated by 0.25 µm digital CMOS technology achieve 2.7 Gcps (gigachips per second) and can multiplex 7 communication channels.

It is important to look for alternative forms of physical movement of people and equipments in order to assure diagnosis and maintenance tasks, especially in an environment where workers are subject to danger. An evident and classical solution is the use of the tele-operation and tele-robotics. If the tele-operation helps to solve a lot of real and technical problems, it still remains insufficient to assure an appropriate remote diagnosis and maintenance. The use of virtual reality techniques with the tele-operation can be the solution for an effective remote maintenance and diagnosis. In this paper we show the inefficiency occurred with the use of only tele-operation in the remote maintenance, we introduce our original new system where we use virtual reality techniques and 2D-3D matching (2D camera image-3D virtual objects) with tele-operation to remotely collect machinery vibration data. We explain its structure, implementation and its advantages. We finished by experimenting the system, measuring the different operating times and precision and discuss the results.

This paper considers a motion planning method for humanoid robots. First, we review a modular state net which is a state net representing behavior of a part of the humanoid robots. Each whole body motion of the humanoid robots is represented by a combination of modular state nets for those parts. In order to obtain a feasible path of the whole body, a timed Petri net is used as an abstracted model of a set of all modular state nets. Next, we show an algorithm for constructing nonlinear dynamics which describes a periodic motion. Finally, we extend the state net in order to represent primitive periodic motions and their transition relation so that we can generate a sequence of primitive periodic motions satisfying a specified task.

The purpose of this study is to design a humanoid robotic hand system that is capable of conveying feelings and sensitivities by finger movement for the non-verbal communication between men and robots in the near future. In this paper, studies have been made in four steps. First, a small-sized and light-weight robotic hand was developed to be used as the humanoid according to the concept of extracting required minimum motor functions and implementing them to the robot. Second, basic characteristics of the movement were checked by experiments, simple feedforward control mechanism was designed based on velocity control, and a system capable of tracking joint time-series change command with arbitrary pattern input was realized. Third, tracking performances with regard to sinusoidal input with different frequencies were studied for evaluation of the system thus realized, and space- and time-related accuracy were investigated. Fourth, the sign language motions were generated as examples of information transmission by finger movement. A series of results thus obtained indicated that this robotic hand is capable of transmitting information promptly with comparatively high accuracy through the movement.

This paper describes the design concept and implementation of a software platform for realization of symbiotic robots that interact intelligently with human in symbiosis manner. Such robots require proper combination of various technologies on a common platform that allows them to work co-operatively. "SPAK" has been developed to serve this purpose. It is a Java-based software platform to support knowledge processing and co-ordination of tasks among several software modules and agents representing the robotic hardware connected on a network. SPAK features frame-based knowledge system, a GUI knowledge building tool, forward and backward chaining engines, networking support, and class libraries for building software agent components. Beside the robotic applications, SPAK can be used as a general-purpose frame system as well. An experimental application of SPAK in human-robot interaction is also given.

An adaptive backstepping and high order sliding modes control algorithm is proposed for output tracking of mobile robots. The controller can greatly reduce the chattering due to conventional sliding modes technique. The proposed algorithm has certain robustness with respect to the external random disturbances and good adaptability with respect to the parametric uncertainty. The effectiveness of the proposed control strategy is demonstrated by simulations studies.

This paper focuses on a global ultrasonic system for self-localization of a mobile robot. The global ultrasonic system consists of some ultrasonic generators fixed at some arbitrary position in the global coordinates and two receivers in the moving coordinates of the mobile robot. This system is used to obtain the state vector of the mobile robot in the global coordinates from the distance measurement between the ultrasonic generator and the receiver. In order to avoid the cross-talk and to synchronize the ultrasonic sensors, the sequential cuing technique using small-sized radio frequency module is adopted. An extended Kalman filter algorithm is used to process the noisy ultrasonic signal and to estimate the state vector. Computer simulations and experiments are conducted to verify the effectiveness of the proposed global ultrasonic system.

In our previous research, we proposed a nonlinear digital filter to Estimate the Smoothed and Differential values of the sensor inputs by using Sliding mode system (ESDS). This estimator is able to eliminate impulsive noise efficiently from time series data. We applied this filter to processing outputs of robot sensors, and it became possible to perform robust environment recognition. ESDS is designed using a theory of variable structure system (VSS) with sliding mode. In short, ESDS is a nonlinear filter. Therefore, it is very difficult to clarify the behavior of the system analytically. Consequentially, we deal with the step function with impulsive noise as an example, and we attempt to eliminate this impulsive noise by keeping the sudden shift of signals. In this case, there is a trade-off between the noise elimination ability and the tracking performance for an input signal. Although ESDS is a nonlinear filter, it has the same trade-off as linear filters such as a low-pass filter. In order to satisfy these two conditions simultaneously, we use two filters whose parameters are independent of each other. Furthermore, in order to repress the adverse affect of impulsive noise in the steady-state, we introduced the boundary layer. Generally, a boundary layer is used so as to inhibit the harmful effect of chattering. Chattering is caused in the sliding mode system when the state of the system vibrates on the switching line of a sliding mode system. By introducing the boundary layer to ESDS, we can repress the adverse effect of impulsive noise in the steady-state. According to these considerations, we clarify the relationship between these characteristics of ESDS and the arbitrary parameters.

A computed torque controller for a dynamic model of nonholonomic mobile robots with bounded external disturbance is proposed to treat the adaptive tracking control problem using the separated design method. A velocity controller is first designed for the kinematic steering system to make the tracking error approaching to zero asympotically. Then, a computed torque controller is designed such that the true mobile robot velocity converges to the desired velocity controller. In each step, the controllers are designed independently, and this will simplify the design of controllers. A novel stability analysis involving the estimation of some differential inequalities is also given to guarantee the stability of the closed-loop system. Moreover, the regulation problem and the tracking problem will be treated using the proposed controller. In particular, the mobile robots can globally follow any path such as a straight-line, a circle and the path approaching to the origin. Furthermore, the problems of back-into-garage parking and the parallel parking problem can also be solved using the proposed controller. Some interesting simulation results are given to illustrate the effectiveness of the proposed tracking control law.

In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.