Fiducial markers enable reliable service robot control. In human-robot coexistence environments, efficient placement of square or circular markers can be challenging due to limited space. In this study, we developed a world-first, elongated fiducial marker, capable of high-accuracy 6-DoF measurements, designed to be installable in tight spaces. We introduced two types of lenticular angle gauges to enhance pose estimation and developed new marker patterns and measurement algorithms to maintain recognition distance and accuracy. The proposed marker achieved a measurement accuracy of 0.1% position error and 0.5° orientation error. This technology will enhance the practicality and applicability of fiducial markers, contributing to the creation of robot-friendly space for future service robots.

Musculoskeletal diseases such as low back pain may cause physical disabilities and make it difficult to continue working. From this point of view, logging of workers' conditions in the working environment is an important issue. So far, limited information acquisition such as smart watches has been realized, but comprehensive information has not been realized with a single device. Therefore, in this study, we estimated the wearer's state using a device called THINKLET, which can acquire image information and inertial information at the same time. By acquiring the pose of the hand from the image information, we were able to estimate the work. More detailed information can be obtained by calculating the position at the same time. By integrating image information with inertial information, it becomes possible to measure the position and posture of the wearer in the working environment. By complementing each other's sensor information, it is possible to obtain robust and accurate self-location. In this research, we developed these basic technologies and evaluated their performance.

To extend the coverage of people able to receive high-quality rehabilitation, remote rehabilitation is required in addition to traditional face-to-face rehabilitation. Although remote rehabilitation using video conferencing systems has been realized to date, communication through physical sensations such as detailed patient motoring information and manual instructions from the therapist has not yet been realized. Therefore, the ultimate goal of this study was to develop multimodal wearable sensor system to support remote rehabilitation with a somatosensory system. To this end, we conducted a basic study of sensing technology in multimodal wear. Multiple strain sensors were attached to the shoulder to digitize the detailed behavior of the shoulder girdle. It was confirmed that the movement of the scapula can be acquired by this strain sensor. Furthermore, it was confirmed that the combination of strain sensors and an inertia measurement unit can be applied for the motion estimation of the entire upper limb.

Technologies for estimating self-position and orientation are important for both humans and robots. These technologies allow robots to perform tasks such as carrying objects and allow people to reach their destinations. Although self-position estimation technologies using GPS and laser rangefinders have been developed, few methods can be used by both humans and robots. Therefore, we developed a method that can estimate three-dimensional position and orientation using visual markers and an inertia measurement unit (IMU). Self-position can be measured with high accuracy by using a visual marker and monocular camera, but such measurement data is discrete and sparse. In contrast, an IMU can continuously measure acceleration data, but data obtained from an acceleration sensor are double-integrated, which increases position error. By combining visual marker and IMU information, position error calculations based on the acceleration sensor can be corrected, and the movement path of the object can be estimated. In demonstration experiments, the proposed method accurately estimates the three-dimensional movement distance when a person walks about 13 m, with an average error of about 40.3mm.

Lenticular angle gauge (LEAG) is a planar pattern that visualizes relative attitude by the position of the black line, which moves according to the viewing angle. The authors developed a new LEAG in which the direction of movement of the black line is 90◦different from the previous LEAG, and used it to develop a non-square high-accuracy fiducial marker. The new marker realized accurate pose estimation with a position error of 0.15% of the distance and an attitude error of 0.5°. This research contributes to the development of high-accuracy markers with a more flexible design. This paper describes the principle and behavior of the new LEAG, the design of the new fiducial marker, and the pose estimation algorithm, followed by the results of performance verification experiments.

Indoor positioning technology is essential for indoor mobile robots and drones. However, there has never been a general-purpose technology or infrastructure that enables indoor positioning with an accuracy of less than 10 cm. We have developed an attitude measurement method using multiple dynamic moires with a lenticular lens and developed an ultra- high-accuracy visual marker with an attitude estimation error of less than 0.1°. We also developed a calculation method that minimizes the marker position error by reminimizing reprojection error using its good attitude accuracy. We proved that accurate local positioning with a position error of about 1 cm in a marker coordinate system is possible even when a marker is shot from a distance of 10 m. In addition, a demonstration test was performed in a public space, and it was shown that high-accuracy global positioning with a position error of about 10 cm is possible.

Automatic control of mobile robots and robot arms requires techniques for estimating the position and orientation of objects with high accuracy and robustness. Although methods using markers or machine learning have been developed, general-purpose and highly accurate estimations have not been realized. Our team developed a high-accuracy visual marker “LentiMark” for high-accuracy estimations of position and posture, but data are lost when the camera cannot obtain marker images. We therefore developed the Marker-IMU system for integrating visual markers with an inertia measurement unit (IMU). When cameras cannot acquire the image of a visual marker, any missing data are restored from IMU data. However, when calculating positions from acceleration sensor values, sensor error increases estimation error. Therefore, we developed a method for error correction using measurements from before and after the missing data. Evaluation experiments confirm that missing data can be estimated using the proposed method.

Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acrylic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed strongly truthful two-path mechanism can sufficiently elicit true out-edges from each node.

This paper reports the development and evaluation of a simulated environment to perform autonomous tasks using LentiMark augmented reality marker-based pose estimation. Performance of marker-based pose estimation is evaluated in simulation over conditions such as position with respect to camera, yaw and roll of marker, varying illumination and shadow, motion blur and marker occlusion. While results will not directly translate to real world performance, limitations in the marker system's pose estimation accuracy due to pixelisation effects, detection failure due to background confusion under roll, and performance degradation with any marker occlusion are identified.

Visual markers are useful tools that aid autonomous robots in object recognition and pose measurement; however, conventional markers have two fundamental problems in orientation estimation: (1) inaccurate estimation in the frontal direction and (2) pose ambiguity. We previously developed a visual marker “LentiMark” that solves problem (1). In this paper, we propose a solution to problem (2) by improving LentiMark. For this purpose, we use two wavelike slices that change two-tone patterns according to the angle of visual line. The new marker enables stable and robust pose estimation without pose ambiguity. We solve the two biggest problems of visual markers in the framework of small planar markers. We verify the efficiency of our method through experiments.

In this paper, a robotic wheelchair using only a pan-tilt-zoom camera is proposed for developing a robotic system which enables a user to navigate in complex environment at low cost. For realizing the proposed system, a high accuracy visual marker, LentiBar which is capable of achieving a high level of precision in positioning and orienting a robotic wheelchair is utilized and its application to door crossing for a robotic wheelchair is presented. Since the visual marker is attached on the top of the target door through which the wheelchair is going to pass, the typical problems for achieving geometric features of the environment can be easily addressed. Door crossing navigation methods are also explained, which enable automatic and hybrid navigation of the robotic wheelchair. Finally, experimental results show the feasibility of the proposed navigation method and the robotic wheelchair system.

The ultimate role of assistive robots is not only supporting user's physical functions but also improving the quality of user's life. Designing such robots is not so easy, because most of the developers do not know about the real “effect” of assistive robots on the users' lives. We propose a concept design method of assistive robots which focuses on the effects of robots on the entire life of human. We introduce the concept of ICF (International Classification of Functioning, Disability and Health) in order to model the interactions among many factors in human activities. The developer uses the models as design knowledge, and defines the requirements along the use scenarios of the assistive robots. We utilize SysML (Systems Modeling Language) in writing the knowledge and scenarios. We developed a prototype of design support software which implemented the design method. In this paper, we describe the concept and the details of our method using some case examples of a mobility assistance robot, and present the future direction.

In order to let assistive robots be utilized in daily lives, it is important to analyze the daily life of users before development, and to enable users to understand the benefit of the robot after development. In this paper, we focus on the robotic arms for daily support, and firstly show the analysis of daily life in the viewpoint of “hand use.” The specification of robotic arms such as the DoF and payload can be determined based on the analysis. Then we show how to describe the benefit of robotic arms. A prototype of database for assistive device is created to show the benefit of assistive robots together with other information such as who can use, how to use, where to use. We utilize ICF approved by the WHO for such analysis and description.

Motion analysis of human body is indispensable for design and evaluation of assistive technologies. Especially, on-site analyses, which are conducted not in experimental environments but in life circumstances, are important to develop truly beneficial assistive technologies. We developed a portable motion tracking system using high-accuracy AR markers. It enables easy measurement of 6-DOF (six degrees of freedom) motion of multiple tracking points by a single camera. The pose estimation errors are about 5 [mm] in location (depth) and about 2 [deg] in orientation. We applied the prototype of the motion tracker to the on-site motion analysis tasks, and demonstrated its availability in the evaluation of assistive technologies.

Visual markers are useful tools assisting visual recognition of object pose in robotic applications. But they have two fundamental problems in orientation estimation. One is degradation of orientation accuracy in frontal observation. The other is “pose ambiguity” that the orientation cannot be determined uniquely. We previously developed a novel visual marker “LentiMark” which solves the former problem. This time we propose a solution to the latter problem by improving it. We utilize two new moiré patterns to get unique angle information and eliminate the pose ambiguity. The new marker enables stable and robust pose estimation without suffering from the pose ambiguities. The contribution of this study is that we solved the biggest two problems of visual markers, in the framework of small planar markers. We demonstrated the availability of our method through some comparative experiments.

Fiducial markers (AR/visual markers) are still useful tools in many AR/MR applications. But conventional markers have two fundamental problems in orientation estimation. One is degradation of orientation accuracy in frontal observation. The other is “pose ambiguity” where the estimated orientation repeats switching between two values. We previously developed a novel marker “ArrayMark” which uses a microlens array and solved the former problem. This time we propose a practical solution to the latter problem by improving the ArrayMark. We attach an additional reference point to detect the occurrence of invalid estimation, and modify the orientation of the marker by inverting the zenith-angle of the visual-line. This marker enables stable pose estimation from a one-shot image without using any filtering techniques. The method is applicable to conventional markers, too. We demonstrated the availability of this improvement to the pose-ambiguity problem.

In order to develop useful assistive devices, analyses and understanding of human functions are necessary. We recorded the activities of daily living, and classified the usage of both hands using the vocabulary of ICF (International Classification of Functioning, Disability and Health) approved by WHO. We confirmed that the usage of the non-dominant hand is auxiliary and greatly different from that of the dominant hand. This indicates a possibility of a reasonable design of assistive devices, e.g. a prosthetic hand for non-dominant hand which has fewer functions but is cheaper than that for dominant hand.

Visual marker is a useful assistive tool for service robots. Existing planar visual markers have poor accuracy and stability in pose estimation, especially in frontal direction. In this study, we developed a novel visual marker based on a new principle enabling accurate and stable pose estimation even by observation from frontal direction. The marker has moiré patterns which consist of lenticular lens and stripe pattern, which vary their appearance according to visual-line angle of observation. We can extract pose information from the pattern by a single camera. We developed a prototype of the marker and an algorithm for pose estimation, and then demonstrated its superiority to existing markers by some validation tests.

Visual markers are useful assistive tool for service robots. Existing planar visual markers have poor accuracy in pose estimation, especially in frontal direction. We solved this problem by a novel principle using a microlens array. The marker displays a changing moiré pattern according to the visual-line direction. We can extract the pose information from the pattern by image processing using a single camera. The developed marker and the processing algorithm enable high-accuracy pose estimation even by observation from frontal direction. We verified its superiority to the conventional method by some validation tests. The marker is an extension of a marker we previously developed, and is more easily fabricated.

With the aging population trends in developed countries, there is an increased expectation of the application of assistive robots. A variety of robots have been developed to address this challenge. Although there are many technical problems to be solved, the lack of objective evaluation indexes for evaluating the benefit to the users is one of the important problems to be addressed before the commercialization of the assistive robots can be realized. The ICF (International Classification of Functioning, Disability and Health) is a classification of the health components of functioning and disability, and is being utilized in medical, healthcare, and welfare fields. The ICF has a basic framework to evaluate healthcare and welfare equipment, including robots, and it will be the basis of the evaluation indexes for assistive robots. In this paper, we present the development of evaluation indexes, and provide the results of preliminary experiments which were conducted using iARM and RAPUDA robots performing a certain lifting task. The experiments illustrate the issues involved in the development of ICF-based indexes for assitive robots.

This paper employs Input-Output analysis to quantitatively analyze cross-sectoral security interdependency in terms of economic activity. Previous studies using the Inoperability Input-Output Model (IIM) have demonstrated the impact of cross-sectoral security incidents from the viewpoint of interdependency. However, the two primary limitations of these studies are that (1) they do not consider each sector's features in terms of information technology (IT) and information security (IS), and (2) they focus on the damage caused by IS incidents to sectors and do not consider the level of security interdependency itself. The author proposes a practical methodology to measure sectoral IS interdependency by introducing forward linkage and backward linkage analyses into an IIM. The methodology assesses the dependency of each sector with respect to IT and the level of IS measures. Furthermore, the paper applies the methodology to recent statistical economic data of Japanese industrial sectors and illustrates the implications of cross-sectoral security interdependency.

A new method of shape modeling for robotic applications is proposed. We utilize visual ID-tags to acquire local tangent planes of objects, and generate approximate polyhedral models of the environment. We developed an optimization algorithm for estimating tag configurations and a method of forming polyhedral models of objects. Our approach enables easy modeling using a single camera without pose information. The shape models are superimposed on the real environment, and they are used in many robotic tasks. We examined the performance of the modeling method, and demonstrated the effectiveness of the models in robotic applications.

We propose a robot control system utilizing semantic information behind robot-plans. Robots read the semantic information annotated to control programs, and utilize it for proper situation-recognition and rational action-generation. We try to develop a framework based on reusable and sharable representations among robots and humans. To this end, we construct an ontology of task world defining a systematized vocabulary for task models and knowledge processing. We introduce several important ideas for modeling the task world. We demonstrate an example of execution monitoring in a simulated blocks world task. Semantic information serves as a guide for rational and reasonable inference, planning, and diagnosis