It is generally difficult to measure complex shapes such as stairs with high accuracy for indoor environment scanning by the robot. Therefore, we consider the three-dimensional (3D) reconstruction of stairs using Structure from Motion (SfM) and Multi-View Stereo (MVS) which perform 3D reconstruction from images acquired by the robot vision. In this study, we verify whether it is possible to acquire a 3D reconstruction result of stairs by using images shot while ascending and descending the stairs as input to the reconstruction method. To calculate the accuracy of the reconstruction result, we use 3D computer graphics software to generate artificial image data to be applied to the 3D reconstruction. Experimental results show that 3D reconstruction results of the stairs are more accurate by applying both images shot when ascending and descending stairs to the 3D reconstruction methods.

This paper describes the development of PHITS Plugin for calculating an integral radiation dose for the estimation of the effects of radiation on robots. PHITS Plugin is an extension of Choreonoid, and it was developed to contribute to design the system of the robots and to plan remote operations. We discuss the functional requirements to compute the dose distribution for calculating the integral radiation dose during the physical simulation. Also, we demonstrate to calculate the dose distribution by utilizing multiple radiation source examples.

This paper describes a Graphical User Interface (GUI) based operation system for building a classifier based on deep learning and verifying its categorization performance. Currently, we build a structure discrimination method based on deep learning with 3D point cloud to support status awareness of the operator of remotely controlled robot. For building a powerful classifier, the operations like “collection of learning data”, “construction of architecture” and “creation of learning model” are done by trial and error. Therefore, we consider to develop a system to make such complicated operations easier and more efficiently. In this paper, we describe about required functions for helping such operations and explain developed a prototype system in detail.

This paper considers the visual impression in a human robot interaction where a robot imitates the human motion obtained by Microsoft Kinect. In this interaction, a subject swings both arms up and down while Kinect measures the subject's motion and this motion is sent to the robot with a certain delay time. In the above situation, firstly, this paper quantitatively shows the perceptible delay time for a human when the robot motion is delayed with respect to own motion. Next, this paper investigates human impressions when he/she watches the robot motion being not synchronized to his/her motion. Last, the difference of the impressions to the delay and synchronic robot motion is statistically verified.

The purpose of this paper is to derive the suggestion towards to the design guideline of Personal Mobility (PM) for giving a good visual impact for the pedestrian. For this purpose, first of all, pre-experiment has been done to briefly evaluate the impression to the whole body size of PM by using Semantic Differential method and factor analysis. From the results, it turned out that big PM is associated with heavy or high rigidity feeling, and then they had a negative impression. Next, conjoint analysis was done to find the answer to “Which component gives the strongest influences to the pedestrian's impression?” The results showed that pedestrian's impression to the PM is dominated by PM's height. The derived suggestions are as follows: “The height of PM should be low as much as possible” and “The body width should be wide (about 600mm), although the importance of it is low compared to the body height.”.

In this paper, we propose a method of estimating amount of attitude change of stationary sensor node which is important to realize observation of identical spot for a long term based on the tracked position data of underwater landmark generated on underwater image, and then in order to see a target, in here a coral, in the fixed position on image, attitude of captured image is corrected based on the estimated amount of attitude data. Then, we generate underwater landmark based on orientation code which is robust for illumination change caused by wave, and track underwater landmarks sequentially combining the method of position detection by using orientation code matching and the method of position estimation by particle filter. Further, we design the evaluation function using time series images. Then, we estimate amount of attitude change of sensor unit of stationary sensor node by optimizing the evaluation function based on Steepest Descent Method, and correct the attitude of original image with the estimated attitude data. Finally, through some experiments, we prove the effectiveness of our methods and discuss the future work.

This paper presents an image-based position measurement of underwater objects using a low-cost maritime mobile robot with a monocular camera. Visual recognition of distant underwater objects is possible in the water with high transparency, for example, in the coastal sea area of Okinawa prefecture, Japan. In this paper, we apply the proposed method to estimate the position of an old anchor that is at a deep of 30 [m] and is estimated to be hundreds-of-years-old in an archaeological site in Ishigaki Island. We explain the measurement principle of the proposed method and the developed system. Moreover, we describe the experiment conducted off the coast of Ishigaki Island and show the experimental results of the proposed method.

This paper studies the difference between two scenes of a real time motion imitation by two persons: one scene is when the subject feels him/herself imitated and another scene is when the subject does not feel him/herself imitated. And this paper also considers that "Which body parts of the subject are frequently gazed by another person?". There has been researches focused on imitating human motion so far. Many of them discuss how to measure the human motion and to implement it on the artificial agents, namely their research interests is the method to imitate human motion from the viewpoint of the side imitating human motion. However, there are few researches discussing the imitation motion from the view point of the person being imitated. When human or robot imitates the human motion, the imitated motion is not the same as normative motion exactly, there invariably is errors between two motions. Nevertheless, the human feels that the motion of another person or robot is own imitation motion. This research, therefore, considers the definition of the imitation motion from the view point of the person being imitated. For this purpose, the experiment was conducted in the case of human - human interaction, in order to consider the features of the motion which the person being imitated feels as imitation motion. As a results, it turned out that the person tends to feel that motion of another was not imitation motion if a delay of the movement was increased. However, the amplitudes of the motion did not affect the impression of the person being imitated. And, the person being imitated tends to see the tip body part of another person when the person sees the own imitation motion.

This paper introduces an efficient motion planning method for on-road driving of the autonomous vehicles, which is based on the rapidly exploring random tree (RRT) algorithm. RRT is an incremental sampling-based algorithm and is widely used to solve the planning problem of mobile robots. However, due to the meandering path, the inaccurate terminal state, and the slow exploration, it is often inefficient in many applications such as autonomous vehicles. To address these issues and considering the realistic context of on-road autonomous driving, we propose a fast RRT algorithm that introduces a rule-template set based on the traffic scenes and an aggressive extension strategy of search tree. Both improvements lead to a faster and more accurate RRT toward the goal state compared with the basic RRT algorithm. Meanwhile, a model-based prediction postprocess approach is adopted, by which the generated trajectory can be further smoothed and a feasible control sequence for the vehicle would be obtained. Furthermore, in the environments with dynamic obstacles, an integrated approach of the fast RRT algorithm and the configuration-time space can be used to improve the quality of the planned trajectory and the replanning. A large number of experimental results illustrate that our method is fast and efficient in solving planning queries of on-road autonomous driving and demonstrate its superior performances over previous approaches.

This research proposes the motion design for the service robot to give the positive impression to the human. The impression to the robot affects the quality of the service being provided by the robot. Thus the positive impression may maintain or improve the quality of the service. This paper, therefore, considers the normative motion to give the positive impression to users, and implements the motion to the robot and evaluates it. In order to give the positive impression to the users, we assume that if the robot motion is close to the human motion, the users have positive impression. For verifying this assumption, this paper derives the normative motion being mimic the human motion. This normative motions is described on the phase plane. On the phase plane, we can derive the common motion among the subjects. This motion is implemented to the robot and evaluated. By the evaluation experiment, it was confirmed that the impression of the normative motion gives users positive impression.

This research proposes the motion design for the service robot to give the positive impression to the human. This paper, therefore, considers the normative motion being designed for service robot to mimic the human motion. The problems in case of finding out the normative motion from the human motion is how to specify one person's motion from different motions caused by individual physical characteristics. We have solved above mentioned problem by expressing humanmotion on the phase plaen. Individual motions on the phase plane were fitted to the one ellipse by using least-squares method. And we regarded this ellipse as the Normative motion. This ellipse trajectory integrates some motions even if different motions in time domain, and can absorb different motions cycle and amplitude caused by individual physical characteristics and peculiarities. This ellipse trajectory was expressed as the time series functions by inverse mapping from phase to time domain, and they were implemented to the service robot in the simulation to replay the normative motion. Finally, in order to discuss the human impression from replayed standard human motion, the evaluation experiment was conducted. From the experimental results, it was confirmed that the impression of the proposed normative motion gives us positive impression in our experimental condition.

This paper proposes the saddle type human body motion interface for personal mobility. The proposed interface uses not only conventional translational body motion but also twisting motion, and makes easy operation. The saddle is attached on the personal mobility by using the seat post with the universal joint at the floor of the personal mobility. The universal joint has three rotational joints where the potentiometers are attached on each. Hip motion makes these joints rotate. The potentiometers detect these rotations, and then this interface measures the hip translational and twisting motions. This saddle does not support whole user body weight, but it is sandwiched by legs and follows the hip motion. User keeps his/her standing position by strengths own legs. The saddle type interface doesn't need pre-setting for the operation, since this system doesn't require any sensors on the human body. By the basic experimental result, it turned out that proposed interface can operate personal mobility and has a potential for intuitive operation.

The purpose of this research is to develop the sight assistive walker using hand haptic information. The feature of the system is user centered system. This system never gives information while user doesn't manipulate the system. Only when user walks and manipulates the system, the system give an environmental information. Therefore, this system needs user's active intention for moving. The main important tasks while walking such as environmental recognition, path planning and locomotion are made by the user. The system supports a user to create a mental map by only giving the environmental information. The system configurations are as follows. This system is something like a cane, and it has a small passive mobile platform on its tip. The LRF (Laser Range Finder) is used for environmental sensing. The powder brakes are used for generating passive brake forces. In order to let the mobile platform move both lateral and back-forward, the four omni-directional wheels are adopted. By generating brake force based on the distance to the obstacles in the moving direction, user can recognize the environment through the hand haptic information. In particular, this paper shows the design concept, system configuration and experimental results to verify the supporting function.

This paper considers that the framework of intuitive interface of personal motility is the combination of the Intention Extractor and Motion Designer, which are the natural extraction of user intention and the human-like system's motion respectively. This research proposed the motion design which makes mobility's motion to be human-like by focusing on the Motion Designer in this framework. For designing human like motion, we need a certain normative motion to the personal mobility. In order to derive the normative motion, we collected the human motion when human moves to lateral direction. The collected motion data was mapped to the phase plane to eliminate time components. On the phase plane, each subject's motion was almost same ellipse, although they have differences in time domain. Then we did ellipse fitting to get one ellipse as the normative motion. For implementing the normative motion, which was fitted ellipse, to the personal motility, the derived normative motion was converted to time function by using t as a parameter. By this manner, personal mobility moved based on the normative motion. In order to verify whether the normative motion is human like or not, the sensory evaluation experiment by Paired Preference Test was conducted. In this experiment, two motions were prepared. One was derived normative motion, the another was “machine motion” which is controlled by the constant speed control inputs. From the result of the test, it turned out that the proposed normative motion is human-like compared with the constant speed motion.

This paper found out the standard human motion which is exemplar motion for letting robot's motion approximate to human motion, and evaluated the human impression to the robot motion. The problems in case of finding out the standard human motion is how to specify one motion from different motions caused by individual physical characteristics and peculiarities. We have solved above problem by following method. First, the fundamental component was extracted from individual time series motion data by using Desecrate Fourier Transform, in order to remove walking cycle and noises and to easily map a motion to the phase plane. Next, the extracted individual fundamental components were plotted on the phase plane. Afterwards, individual patterns on the phase plane were fitted to the one ellipse by using least-squares method. And we regarded this ellipse as the standard human motion. This ellipse trajectory integrates motions even if different motion in time domain, and can absorb different motion cycle and amplitude caused by individual physical characteristics and peculiarities. Last, this ellipse trajectory was expressed as the time series functions by inverse mapping from phase to time domain, and they were implemented to the robot to replay the standard human motion in the simulation. Finally, in order to discuss the human impression from replayed standard human motion, the evaluation experiment using Semantic Differential (SD) Method was conducted. From the experimental results, the proposed motion design can let the robot motion approximate to the human motion, because the standard human motion is the fundamental motion among individuals and gives positive impression to users.

In this paper, we propose the technique classifying background area automatically by calculating the color variance in the RGB space of the picture. By this technique, the background classification robust to change of the background brightness and the background color was enabled. Moreover, we show the effectiveness of this technique by performing particle detection and verifying the result of counting. We aim at development of the particle detector which performs particle counting automatically about the “dispersion staining method” which is one of the processes of asbestos analysis. We used the pictures taken by phase contrast microscopy used for a “dispersion staining method.”

This paper presents a method for designing classifier to automate an evaluation process of protein crystallization growth states. The classifier is designed by binary decision tree and its classification sequence is decided in descending order of classification accuracy. Furthermore, for applying to automated crystallization system, some functions reducing mis-classification are appended to the classifier. The performance of this method has been evaluated by comparing classification result by using proposed method with the result by manual classification

In our current research, we are developing a holonomic mobile system which is capable of running over the step. This system realizes omni-directional motion on flat floor using special wheels and passes over non-flat ground in forward or backward direction using the passive suspension mechanism. In order to realize the high mobile performance during step climbing, it is required to reduce wheel slippage for maximizing wheel traction. This paper proposes a new plural wheel control method based on wheel slip estimation. Our key idea is estimation of wheel slippage comparing with the loads and rotation velocities of all actuated wheels, and using this result for wheel control of the vehicle for reducing wheel slippage. The controller can adjust control tractions of plural wheels when the wheel begins to slip and can improve the mobile performance of the vehicle. The performance of our proposed control scheme is experimented by computer simulations and experiments

We are conducting research and development in technology for unifying the information of a large number of intelligent sensors distributed in a disaster-stricken area. Operating effectively as a system in real time, network household electric appliances, a personal digital assistant (PDA), a robot, etc. provided information and an action plan for a stricken area's situation. In this paper we introduce ubiquitous device "intelligent data carriers for rescue (IDC-R)" and formation of a dynamic network, and development of a means of disaster information communication (a group of people, a robot, an adhoc network, a home network, etc.). Target experiment for actual proof is also introduced

In our current research, we are developing a holonomic mobile system which is capable of running over the step. This system realizes omni-directional motion on flat floor using special wheels and passes over non-flat ground in forward or backward direction using the passive suspension mechanism. The vehicle changes its body shape according to ground condition when it runs over the rough terrain. This paper proposes a new wheel control method according to the change of the vehicle's body shape for passing over the step. Our key idea is the modification of its kinematic model referring to the body configuration dynamically and using this model for wheel control of the vehicle. The controller can adjust the wheel control references when the vehicle passes over the rough terrain changing the body shape and can reduce the slippage and the rotation error of the wheels. The performance of our proposed method is experimented by computer simulations and experiments.

In this paper, we propose a non-invasive and nonrestrictive human vital-signs sensory system using an electrically-conductive fiber. To maintain health, it is important to monitor our physical condition daily. Even during sleep, a person's condition is not constant and sometimes it can change suddenly. Therefore, we are developing a human vital-signs sensory system for monitoring purposes. Our proposed system consists of a sensing part, data collection part and signal processing part. The sensing part is composed of electrically-conductive fibers incorporated in a conventional bed sheet. Since the sensors are arranged in a grid structure, we can detect the pressure distribution on the sheet. We utilize this sheet to acquire human vital signs such as respiration and heart rate continuously. With a preliminary prototype, we show the basic properties of the proposed sensory system.

This paper proposes a technique of the extended template matching that detects the object with two-dimensional standard shape under the outdoor environment. Under the outdoor environment, the intensity distribution of the object changes variously by the change in the lighting condition and the change in the surface properties of the object. On the other hand, the conventional template matching assumes the intensity distribution of the object does not change. Therefore, it proposes the technique for evaluating plural similarities calculated with partial and whole templates by the linear discriminant function. The effectiveness of the proposed technique was confirmed by the detection experiment of the corner fitting of the container under the outdoor environment.

In recent years, several projects have advanced the research and development related to the automation of the protein crystallization process. However, the evaluation of crystallization states has not been completely automated yet. In the usual crystallization process, the researchers evaluate the crystallization growth states of the protein solution samples based on one's visual impressions and assign them a score over and over again. Then it is required to make the work more efficient. The method presented here automates such evaluations. This method attempts to categorize the individual crystallization droplet images into five classes from A to E, based on their crystallization states. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process utilizing linear discriminant analysis. The performance of this method has been tested on our experiments using actual protein solution images.

In this study, we propose a new holonomic mobile mechanism which is capable of step-climbing. This mechanism realizes to move in any direction on the flat floor and pass over steps and slopes in one (fore and hind) direction. The vehicle equips with seven omni-directional wheels with cylindrical free rollers and a passive body axis that provides to change the shape of the body on the rough terrain. There is no need for additional actuators or sensors for the passive body axis. In our previous work, we constructed a prototype mobile mechanism and analyzed its kinematic characteristics. In this paper, we design a new control scheme and improve the mechanism for passing over the steps more stably, The performance of the prototype system is verified through experiments and computer simulations.

We propose a decentralized motion control algorithm of multiple mobile robots transporting a single object in cooperation with a human based on a path generated from an environment. Each mobile robot is controlled as if it had caster-like dynamics around a representative point attached to the object and its representative point moves along the path based on the intentional force applied by a human. The proposed decentralized control algorithm is experimentally applied to three omni-directional mobile robots referred to as DR Helper. Experimental results illustrate the validity of the proposed control algorithm.

Considering that intelligent robotic systems work in a real environment, it is important to equip the function of self-condition recognition in such systems. From this point of view, we discuss the way to diagnose such systems and construct a self-diagnostic system for an autonomous mobile robot. The system must have a self-contained type diagnosing system. In our previous work (2001), we constructed a state sensory system and examined the coping behaviors to self-condition. In this paper, we describe an extension of the state sensory system and diagnosing experiment using an autonomous mobile robot. The prototype of our sensory system consists of internal and external sensors. We show the experimental results using an omnidirectional mobile robot and confirm the effectiveness of our proposed system.

We propose a decentralized motion control algorithm of multiple mobile robots transporting a single object in cooperation with a human based on a path generated from an environment. Each mobile robot is controlled as if it has a wheel with a free rotational joint and moves along the path based on the intentional force applied by a human. The proposed decentralized control algorithm is experimentally applied to the omni-directional mobile robots referred to as DR Helpers. Experimental results illustrate the validity of the proposed control algorithm.

We propose a decentralized control system of multiple mobile robots transporting a single object in coordination without using force/torque sensors. In this system, each robot is controlled as if it has a specified impedance dynamics without using a force/torque sensor. The robots transport an object in coordination using a leader-follower type control algorithm. The effect of parameter identification errors is discussed and a method to reduce it is proposed. The proposed control algorithm is experimentally applied to two mobile robots, and the experimental results illustrate the validity of the proposed control algorithm.

We propose a decentralized control algorithm of multiple robot helpers, called DR Helpers, which have been developed for manipulating a large object in cooperation with humans. In this algorithm, each robot is controlled as if it has a caster-like dynamics around a representative point attached to the object, and manipulation of a large or long object in cooperation with a human can be realized easily. The proposed control algorithm is experimentally applied to two DR Helpers and experimental results illustrate the validity of the proposed control algorithm.

We propose a decentralized control algorithm of multiple mobile robots transporting a single object in coordination. In this algorithm, each robot is controlled as if it has a caster-like mechanism and transportation of a single object by multiple mobile robots is realized without using the geometric relations among robots. The proposed control algorithm is experimentally applied to two mobile robots. Experimental results illustrate the validity of the proposed control algorithm.

We propose a behavior learning method of a mobile robot using visual information. The proposed system consists of two networks for the state expression and the behavior generation. The state expression network, which is based on self-organizing algorithm, categorizes the robot's states. Also, the behavior generation network acquires the robot's behaviors using the output of the state expression network. Here, for training the behavior generation network, we utilize the actor-critic method as a kind of unsupervised learning scheme. We report some experimental results using the mobile robot in real environment. The mobile robot generates adaptive behaviors utilizing visual information.

In, this paper, we discuss a problem relating to the force/moment transformation for the handling of a large object by multiple mobile robots in coordination. We propose a control algorithm using geometrical constraints among the grasping points and the representative point of the object, which reduce the effect of noise amplified by force/moment transformation. We extend this algorithm to the decentralized control algorithm of multiple robots handling an object in coordination. The proposed control algorithm is experimentally applied to the mobile robots. Experimental results illustrate the validity of the proposed control algorithm.

This paper discusses the problems in teleoperation systems for mobile robots and the use of a virtual world in these systems. In order to achieve smooth operation of the mobile robot via the communication link, we should consider the delays in data transfer. To compensate the incomplete data sets, virtual images can be generated by computer graphics when the information of the working environment can be acquired beforehand. We construct a virtual world teleoperation system. The performance of the system is examined through experiments with an actual mobile robot. It is shown that the virtual robot can be operated by a human operator in almost the same manner as a teleoperated real robot.

In this paper, we propose a new mobile robot architecture with a body force sensor. The body force sensor is the force/torque sensor which is located between the drive mechanism and the body of the mobile robot. The use of the body force sensor almost realizes the collocation of sensors and actuators, and makes the whole body of the mobile robot sensitive to external force/moment. Decentralized motion control algorithm for handling a single object by multiple robots in coordination is implemented in each mobile robot and stable handling of an object is realized by multiple mobile robots with the body force sensor. Experimental results illustrate the validity of the proposed architecture.