This reserch newly proposes an augmented reality system called "composite marker" to recognize curved objects obstructing within mobile robot workspace. Fundamental experiments are carried out to identify a sphere to verify its effectiveness.

This paper proposes binocular active free space optics system. It presents the scheme of optical mesh network, and configuration of laser beam alignment system. Active free space optics apparatus is newly designed to facilitate its installation. Ray tracing simulations provide the desirable optical system for long distance transmission. A prototype of the binocular active free space optics mechanism is constructed for some fundamental experiments. The results disclose elementally features of the mechanism. They also clarify the ability of the proposed system by evaluating the quality of transmission.

This paper describes the aerial laser transmission techniques applied to mobile robot communication by constituting optical mesh network. Active free space optics system is newly proposed to transmit bilateral laser beams which are equipped with motorized mirrors, photodiodes, and position sensitive detectors. The alignment tactics are presented with regard to the coarse and fine positioning techniques of laser beam. Experiments prove that the proposed techniques enable the transmission laser beam to locate the target receiver precisely and to track the swaying transmission apparatus.

A new augmented reality system is proposed to apply to robot navigation in this paper. It generates pattern IDs by analyzing infrared optical markers projected in the real world. The system enables the robot to guide a complicated motion by the robot commands corresponding to the pattern IDs. A prototype system is constructed to conduct fundamental experiments of remote robot navigation.

This paper studies an active free space optics technology to realize an optical mesh network. A laser beam control system is designed, and optical axis control technique is evaluated in terms of both telecommunication and control performances. A transmission line switching technique is also proposed and experimented with a minimum configuration prototype system. Results prove that the active free space optics system can establish broadband communication at 0.5 Gbps with a bit error rate of 10-10, and can successfully switch the transmission line within 0.01 second at least.

This paper proposes a method for interpretation of fuzzy voice commands based on the vocal cues. The fuzzy voice commands include fuzzy linguistic terms like “little” and quantitative meaning of such terms depends on the environmental conditions. Therefore the robot's perception of the corresponding environment is modified by acquiring the user's perception through a series of vocal cues. The user's willingness to change the robot's perception is identified based on the vocal cues to improve the adaptation process. The primitive behaviors related to the end-effector movements of a robot manipulator are considered and evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues to adapt the robot's perception by modifying the BEN. The user's satisfactory level for the robot's movements is utilized to track the satisfaction of the user. A situation of cooperative rearrangement of the user's working space is used to illustrate the proposed system by a PA-10 robot manipulator.

In a stabilizing control for nonholonomic mobile robots with two independent driving wheels, a nonholonomic double integrator in the kinematic model is first considered as a controlled object model. Then, a quasi-continuous exponential stabilizing control method is proposed as one of underactuated control methods by using invariant manifold theory. Next, to extend the velocity input control in a kinematic level to the torque input control in a dynamical level, an extended nonholonomic double integrator consisting of the kinematic and dynamical models is treated as a controlled object model. A quasi-continuous exponential stabilizing controller is further derived for such an extended model by using the same way as used in the kinematic level control. The effectiveness of the present method is proved with some demonstrative simulations.

This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy linguistic information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy linguistic information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy linguistic information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy linguistic information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.

In this paper, an obstacle avoidance method is described for a quadruped walking robot by using camera image and ultrasonic sensors. A single camera is used to acquire the rough information of three-dimensional obstacle, while some ultrasonic sensors are used to obtain the accurate information of the obstacle. Thus, the obstacle is efficiently measured by combining these sensors properly. The effectiveness of the proposed method is verified for several experiments, in which TITAN-VIII is adopted as an experimental setup. The quadruped robot can arrives at the target with repeat of these actions. The effectiveness of the presented method is verified through some experiments

When an intelligent robot works together with human in a living space, we may often encounter a scene that the robot supports the human behaviors. Understanding of the human behavior is required for the robot to judge what kind of action serves as assistance to the human in such a scene. The present research aims at constructing a system that can understand the human behavior using the time series images for the movement of the human, which can be obtained from an active vision head. The first process estimates the human posture in each frame. The next process divides the basic human action into a posture and a position. The final process estimates the human behavior by examining the action and the target, under the assumption that the human behavior consists of the combination of an action and a behavioral target

A switching control method based on fuzzy energy regions is proposed for controlling underactuated manipulators. We also consider the application of a logic-based approach, which was originally developed for a nonholonomic system without drift terms, to the underactuated manipulators with drift terms as considered in this research. Then we compare a switching control method based on a logic method with that based on the present fuzzy energy regions. The effectiveness of both methods is demonstrated with some simulations

A novel online inverse kinematics solution of redundant manipulator to avoid joint limits is presented. A Widrow-Hoff neural network (NN) with a learning algorithm derived by applying Lyapunov approach is introduced for this problem. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. The joint velocity limits as well as the joint limits are incorporated into this method. Experiments are conducted for the PA-10 redundant arm to demonstrate the efficacy of the proposed control system. A comparative study is made with the gradient projection method.

A backpropagation neural network (NN) is presented for the inverse kinematic problem to obtain a position control system for manipulators with multiple degrees-of-redundancy, where information provided from a laser transducer at the end-effector is used for planning the trajectory. A fuzzy reasoning system is designed to generate an approximate joint angle vector, because the inverse kinematics in this problem has infinite number of solution vectors. This vector is fed into the NN as a hint input vector rather than as a training vector to limit and guide the searching space. Simulations are implemented on a four-link redundant planar manipulator to show that the present control system is capable of tracking the planned trajectory while avoiding the collision.

In walking of quadruped robots, a better performance is obtained by changing the order of swing leg at a gait. In this paper, we study a walking with obstacles avoidance of a quadruped robot using neural network (NN) for determining the moving distance of the robot and for determining the order of a swing leg. The training data is considered with initial positions of each leg. The effectiveness of the present method is demonstrated with some experiments using TITAN-Vin.

In this paper, we investigate the viability of multilayered neural network (NN)-based extension of a conventional "perception" control concept. The perception process selects and completes the information from the system to be controlled before passing it to the controlling agent so that control is not lost when sensory information from the system is incomplete. The perception process produces an expectation of the next set of information to be received from the system. The expectation is used to replace missing parts of the information received and it also influences the next perception. In the existing work, each of the expectation elements is linearly acquired such that the expectation tells only the dominant information in the recent past, i.e., this approach has no capability to sense the trend and the dynamics in the information. This handicap could become a serious problem when the perception process is applied to real physical systems. Here, we introduce an extension of the perception control process by using a radial basis function (RBF) feedforward NN to learn the trend and the dynamics in the information and produce the expectation of the next observation. Through some simulation comparisons, we show that the proposed RBFNN-based method is better than the existing one.

We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.

A concept called "common reference object" is proposed for cooperative transportation and decentralized system consisting of multiple noholonomic mobile robots is constructed to demonstrate the present concept. In this system, one agent acts as the leader, which is able to plan and manipulate the omnidirectional motion of the object. Other agents referred to, as followers are equipped with compliance arms and cooperatively transport the object by keeping a constant position relative to the object. During transportation operation, the leader robot can not only plan the motion of the object but also broadcast the local velocity of the object to other agents. Then, each follower receives such information and generates its own velocity in the local coordinate using a mapping process. In this paper, neural network (NN) and genetic algorithm (GA) are tested in identifying the mapping process. Simulation results show a good performance of the present system.

Recently robot manipulators have been expected to perform sophisticated tasks such as object manipulation, assembly tasks, or cooperative tasks with human workers. In order to realize these tasks with robot manipulators, it is important to understand the human strategy of object grasping and manipulation. In this study, we have examined how a human being decides the grasping force necessary to manipulate an unknown object in order to apply human object-grasping strategy for robotic systems. Experiments have been performed with several kinds of objects under several kinds of conditions to investigate how much grasping force human subjects generate. Adjustment strategy of human grasping force when the object is manipulated or in contact with an environment is also examined. Neural networks (the desired grasping force planner) that generate the humanlike desired grasping force are then designed for robotic systems. The effectiveness of the proposed desired grasping force planner is evaluated via experiments.

This paper presents a new concept for solving fuzzy controller stability problems on matrix inequalities via evolutionary computation (EC). The gain scheduling problem of a multi-model fuzzy system, which satisfies the Lyapunov stability criteria, is solved. The generalized eigenvalue problem can be directly introduced into EC in searching positive definite or positive semi-definite matrices, by making a penalty for an individual, that violates the inequality condition in order to solve the nonlinear constraints or linear matrix inequalities. Examples are given to illustrate the effectiveness of the proposed methodology.

An adaptive actor-critic algorithm is proposed under the assumption that a predictive model is available and only the measurement at time k is used to update the learning algorithms. Two value-functions are realized as a pure static mapping, according to the fact that they can be reduced to nonlinear current estimators, which can be easily constructed by using any artificial neural networks (NNs) with sigmoidal function or radial basis function (RBF), if all the inputs to the present value-functions are based on simulated experiences generated from the predictive model. In addition, if a predictive model is assumed to be used to construct a model-based actor (MBA) in the framework of adaptive actor-critic approach, then this type of MBA can be viewed as a network whose connection weights are composed of the elements of feedback gain matrix, so that the temporal difference (TD) learning can also be naturally applied to update the weights of the actor. Since the present method can update the learning by using only one measurement at time k, a relatively fast learning is expected, compared with the previous approach that needs two measurements at times k and k + 1 to update the actor-critic networks. The effectiveness of the proposed approach is illustrated by simulating a trajectory-tracking control problem for a nonholonomic mobile robot.

We (2001) have proposed the "rings gymnastic robot" aiming at an application to the gymnastic coaching by understanding the ring exercises through the robot. The ring exercises have the characteristics that the apparatus can move in all directions freely unlike the other gymnastic events. Fuzzy control is adopted as a control method for the rings gymnastic robot. As the joint torque in performance is useful for the coaching and gymnasts can easily understand the skill to realize performance if the control method is represented as "if-then" rules. In this paper, we derive a three-dimensional model of the robot and acquire the performance skill of a backward giant circle using the genetic algorithm. Three-dimensional graphics sequence based on simulation results shows that the obtained skill is effective.

A novel concept for controlling of nonlinear systems using chaos and fuzzy model-based regulators is presented. In the control of such systems, we employ two phases, the first of which uses open-loop control forming a chaotic attractor or using chaotic inherent features in a system itself. Once the system states reach a predefined convex domain, open-loop control is cut off and a fuzzy model-based controller is employed under state feedback control in the second phase. The relaxed stability conditions and linear matrix inequalities (LMIs)-based design for a fuzzy regulator is introduced to construct a fuzzy attractive domain, in which a global solution is obtained so as to achieve the desired stability condition of the closed-loop system. The proposed controller architecture has been tested using three nonlinear systems: the Henon map, the Lorenz attractor, and a two-link manipulator. The simulation results show the effectiveness of the proposed controller.

A furniture polishing robot which uses a trajectory generator based on cutter location data, is proposed. The position and orientation of the polishing tool is suitably controlled by referring to the output from the trajectory generator. The trajectory generator yields a zigzag path, whirl path or their combinational path according to the shape of each workpiece. Using the trajectory generator, the present polishing robot doesn't need the conventional complicated teaching process. The contact force acting between the polishing tool and the workpiece is controlled through a desired impedance model to follow in Cartesian space. The effectiveness and promise of the proposed polishing robot are demonstrated through some experiments concerning polishing tasks using an industrial robot JS-10 with a PC based controller.

The ring exercises have the characteristics that the apparatus can move in all directions freely unlike the horizontal bar exercises with the constraint between hands and the horizontal bar. If a robot that performs rings exercise is realized practically by clarifying the motion control method, an application to the coaching can be expected. Therefore, we propose a novel "rings gymnastic robot" in this paper. We acquire fuzzy rules to realize a series of exercises that is a handstand through a backward giant circle by the genetic algorithm, because the rules are considered to be useful for coaching. The effectiveness of the fuzzy controller is confirmed through a simulation using a derived dynamics of two-link rings gymnastic robot.

We have been developing exoskeletal robots in order to realize the human motion support (especially for physically weak people). We propose a 2 DOF exoskeletal robot to support the human shoulder motion. In this exoskeletal robot, the flexion-extension and abduction-adduction motions of the shoulder are supported by activating the arm holder of the robot, which is attached to the upper arm of the human subject, using wires driven by DC motors. A fuzzy controller has been designed to control the robot according to skin surface electromyogram (EMG) signals in which intention of the human subject is reflected. The proposed controller controls the flexion-extension and abduction-adduction motion of the human subject. The effectiveness of the proposed exoskeletal robot has been evaluated by experiment.

In order to help everyday life of physically weak people, we are developing exoskeletal robots for human (especially for physically weak people) motion support. In this paper, we propose a one degree-of-freedom (1 DOF) exoskeletal robot and its control system to support the human elbow motion. The proposed controller controls the angular position and impedance of the exoskeletal robot system based on biological signals that reflect the human subject's intention. The skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion have been fused and used as input information of the controller. In order to make the robot flexible enough to deal with vague biological signal such as EMG, fuzzy neuro control has been applied to the controller. The experimental results show the effectiveness of the proposed exoskeletal robot system.

This paper presents an effective force control method in which multiple fuzzy-neuro force controllers are suitably and automatically combined with a proper rate in accordance with the unknown dynamics of an environment. The optimal combination rate of the fuzzy-neuro force controllers according to the environment dynamics is defined online by a neural network which is off-line trained with genetic algorithms. The effectiveness of the proposed method has been evaluated by computer simulation.

A fuzzy behavior-based system, which act as a planning system, is realized for the task control of a six-degree of freedom PUMA robot manipulator. A fuzzy behavior control system that was applied to a three-link manipulator by Dassanayake et al. (1999) is modified as a planning system for the PUMA robot. Fuzzy behavior elements are trained by a genetic algorithm. This has been conducted for three behavior groups namely objective behavior group, free behavior group and reactive behavior group. Simulation has been carried out for the PUMA robot to reach a target from a given point while avoiding an obstacle. Result shows that the fuzzy behavior based approach can be applied to plan the manipulator's joint angles and angular velocities to reach a particular point while avoiding obstacles.

We are stepping into an aging society rapidly. In that society, it is important that physically weak people are able to take care of themselves. In this paper, we introduce a 1DOF exoskeletal robot to support the elbow motion of physically weak people. Fuzzy control has been applied to control the exoskeletal robot system based on vague biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated force by the human subject's wrist during the human elbow motion have been used as input information of the fuzzy controller.

Intelligent control is being widely studied in the field of robotics. A construction method of an intelligent control system is the fuzzy behavior-based control which decomposes a task into each elemental behavior like a subsumption architecture, and each elemental behavior is realized by fuzzy reasoning. In the paper, a module learning method is proposed for such a system, because the robot will be able to get more general knowledge or fuzzy reasoning than a central learning method. In particular, the module learning method is applied to an obstacle avoidance problem of a mobile robot. The effectiveness of the present method is illustrated through some simulations.

Impedance control allows the manipulator to change the mechanical impedance such as inertia, damping and stiffness, acting between the end-effector and its environment. However, to achieve stable force control under unknown stiff environments, complicated tuning of desired impedance parameters is needed. Among the parameters, the desired damping is the most significant to suppress overshoots and oscillations. In the paper generalized fuzzy environment models with anisotropy are proposed to systematically determine the desired damping against unknown environments. The models learned with genetic algorithms, can estimate each directional stiffness of the environment and yield the desired damping, considering the critical damping condition of the control system. Position and force control simulations are shown to demonstrate the effectiveness and promise of the models.

Friction of each joint of a robot manipulator has to be effectively compensated for in order to realize precise position/force control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks, and genetic algorithms) have played an important role in the control of robots. By applying soft computing techniques, learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which uncertain/unknown dynamics of the environment are compensated for in the task space and joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.

A position based impedance control with a fuzzy compensator is proposed to improve a force control performance, in which the environment has unknown dynamics or curved surface. The main attraction of the fuzzy compensator is that it generates proper position compensation as feedforward command so that overshoots and oscillations in the direction of force control can be suppressed satisfactorily. Profiling control simulations using the dynamics of the PUMA560 manipulator are shown to demonstrate the effectiveness and promise of the fuzzy compensator under the environment having unknown dynamic characteristics.

We describe a method for the path planning of an omnidirectional mobile manipulator by applying an evolutionary strategy. For a path using a B-spline, it is important how to choose some appropriate data points and the corresponding end points. The proposed approach automatically selects their points in a wide range, minimizing or maximizing the total cost function, which consists of several sub-cost functions such as motion smoothness, movable range of joint, singular orientation and falling down. The simulation result shows that our method is effective for the path planning of a robot, which has many boundary conditions and complicated evaluation such as the mobile manipulator considered.

We describe a coordinative motion control based on the master/slave method for different kinds of manipulators. The master side consists of a pantagraph-type two link manipulator driven by a torque command, whereas the slave side is an industrial manipulator driven by a position command with a force/torque sensor. It is difficult to maintain a desired contact force by only a master/slave method. To maintain a desired contact force, a compensative position command is generated by using fuzzy reasoning with the force sensor information. The effectiveness is verified by experiments.

We have already developed a fuzzy behavior-based control system that combines the concept of subsumption architecture and fuzzy reasoning technique. When applying it to a mobile robot, the robot needs to have precise information such as distance and azimuth. We discuss how to construct the fuzzy behavior-based control system for a miniature mobile robot in the case when the robot can not often receive any information from sensors. In addition, we apply a virus evolutionary genetic algorithm with species to generating the fuzzy rule. The effectiveness of the proposed method is shown by simulations.

A fuzzy servo system is described for a system with noises by using a stochastic fuzzy control method with some linear dynamic models. The fuzzy servo approach is applied to the control of rotational angle for an omnidirectional mobile robot with three orthogonal-wheel assemblies. The stochastic fuzzy servo method and its modified method with a static evaluation on the model confidence are implemented for some simulations of the mobile robot. In particular, two improvement methods are shown for the case when a set of the prespecified linear models does not include a model comparable to the reference rotational angle.