This paper presents the Funabot-Suit for the Upper Body, enabling users to perceive seven upper body motions (shoulder abduction, elbow flexion/extension, and trunk rotation) through kinesthetic feedback induced by 80 McKibben artificial muscles embedded in elastic clothing. Unlike existing systems that rely on rigid structures or vibrotactile feedback, the Funabot-Suit provides soft, multi-joint kinesthetic feedback via garment deformation. Building on our previous work focused on trunk motion, this study extends perception to more localized joints such as the shoulders and elbows. The experimental results from nine participants indicate that larger body parts tend to induce stronger and clearer sensations, while certain localized motions, such as elbow extension, are perceived less clearly. These findings suggest that both the size of the body part and the placement of artificial muscles affect kinesthetic perception. The results provide design insights for future wearable devices targeting VR, rehabilitation, and human-robot interaction.

This paper presents a wearable haptic device that can perceive eight directions of the forearm utilizing cloth deformation. Inducing kinesthetic perception through wearable haptic devices is expected to have applications in VR, rehabilitation, and teleoperation. The demand for haptic devices made of flexible materials that can provide intuitive feedback has rapidly increased. In this paper, we developed a novel haptic device for the forearm that consists of thin artificial muscles and existing clothing. The cloth deformation caused by the contraction of the artificial muscle allows the wearer to perceive four directions of the forearm, and the results of the perception experiment showed 98.8% accuracy. Furthermore, experimental results showed that synthesis stimuli can extend the wearer’s kinesthetic perception in eight directions. The findings provide important guidelines for the design of future haptic devices.

This manuscript investigates the impact of unmanned aerial vehicles (UAVs) and their cameras’ key specifications on the efficiency of infrastructure visual inspections using UAVs. The challenges of UAV-based inspection data collection include reducing necessary waypoints to improve inspection efficiency while collecting desired data for sure. Existing studies generally concentrated on optimizing the efficiency of UAVs’ inspection trajectories and waypoints when equipment is given. However, the impacts of equipment specifications and their selections have not been investigated, yet they also significantly affect inspection efficiency. This manuscript surveys the key specifications of current equipment available for inspection and investigates the variation in efficiency through inspection simulations with different equipment specification combinations. Simulation results reveal that with the highest pose accuracy achievable by existing UAV platforms, it is possible to achieve efficient inspection. However, such high pose accuracy may be difficult to achieve, underscoring the importance of targeted selection of camera focal lengths according to UAV specifications and the types of pose errors. Additionally, using higher-resolution cameras may substantially improve the efficiency of high-resolution data collection tasks, which is a more practical solution for enhancing efficiency than further improvements in UAV pose accuracy. The conclusions drawn from this research provide actionable guidelines for equipment selection and development and lay the foundations of efficiency modeling, ultimately contributing to the advancement of UAV-based inspection methodologies.

Compliance is important for the rehabilitation of wearable assistive robots. Various methods have been studied to improve the compliance of wearable robots, such that the use of impedance control of actuators and soft actuators with high compliance. On the other hand, for robots whose degrees of freedom of movement are limited by rotational axes like conventional robots, compliance performance may deteriorate due to mounting misalignment even if the actuator is designed optimally. Soft wearable devices that do not have rigid joints and are composed of soft base materials also exist, but the force is dispersed and cannot exert great force. Our Funabot- Plate consists of a base cloth, McKibben-type pneumatic artificial muscles, and thin, rigid plates that efficiently transfer the exerted force of the artificial muscles to the human body. Thus, Funabot-Plate expects a high output efficiency while high compliance. To verify the structural validity of the Funabot- Plate, two experiments were conducted to quantitatively compare its compliance characteristics with the rigid-frame robot. Through the measurement experiments, we were able to quantitatively evaluate that the structure of Funabot- Plate exhibited higher compliance in both conditions, transparent mode and assist mode, when compared to conventional rigid-frame robots.

Fabric actuator (Funabot) is expected to be applied to the haptic device. On the other hand, conventional control method in fabric actuator only feeding back the surface shape by depth sensor, although haptic device evokes the tactile via the direct contact between the device and the human body. We developed the haptic device for grabbing and established the leader-follower system that include force distribution sensors and realized measuring the leader-side tactile as the distribution information, sending the follower-side and tracking the static distribution by feed-back control at the follower-side. The basic performance was verified by two experiments with the soft torso made of silicone in the static condition. The first experiment clarified that our device had an independent controllable tightening force of at least 7.9 N for seven channels in a 2 cm width. The second experiment proved that our device could track the leader tactile force within the error of 0.21 N in most cases. While the tracking performance was admitted, response performance was slow because of the characteristics of artificial muscle.

Assistive robots with soft actuators do not have joints like exoskeletons and have attracted attention because of their affinity for humans. In this study, we propose a soft assistive suit called "Funabot-Plate," which is driven by artificial muscles, has no joints, and is partially equipped with rigid plates and contact force distribution sensors. Funabot-Plate is expected to be applied to rehabilitation in the future, using contact force distribution information to evaluate and control suit safety. Assistive torque was designed based on the geometric model. Joint angle estimation and control were performed using contact force distribution sensors. The range of motion, contact force on the wearer, and convergence performance of the step response were evaluated through several basic experiments. As a result, it was confirmed that the contact force was measured normally, and the response converged with a slight error of about 1.6 deg. to the command value, although the range of motion remained a problem.

In Japan, where the population is aging, traffic accidents caused by elderly drivers have become a social problem. The main cause of the accident is a decline in cognitive ability, and there is an urgent need to develop technology for early detection of decline in the ability. The authors have been developing a technology to evaluate the cognitive ability of a driver from the driving behavior data. In the driving behavior data, there are some scenes where the cognitive ability can be evaluated and some scenes where the cognitive ability cannot be evaluated. Therefore, in this paper, we propose a clustering method for driving behavior data. Here, it is considered that the cognitive ability is the accuracy of the operation for the surrounding situation, and it is useful to cluster the relationship pattern between the situation and the operation for the driving behavior data as a group. In this method, Coupled-GP-HSMM and Convolutional Variational Auto Encoder are applied as a time series clustering method. This method realizes clustering of the relationship pattern between the time-series data representing the situation and the time-series data representing the operation.

To improve safety of wearable assist robots, the authors make a prototype wearable robot suitable for the contact force distribution feedback control system. Because wearable assist robots contact the user and apply force directly, ensuring safety of the user is most important. In order to improve safety from the aspect of control, a contact force distribution feedback control system has been researched. The system controls contact force between the user and a robot directly. Although the system validated in principal with a simple robot model on simulation, validation with an actual robot has not done yet. There are no wearable robots measurable the contact force distribution with user. In order to measure the contact force distribution for the control system, two points: measurement reproducibility and measurement as distribution, are necessary. In this paper, a prototype robot that satisfies the two points is made. The robot is confirmed to have the two points necessary for the system by measurement experiment.

This paper evaluates our measuring position determination method of Unmanned Aerial Vehicles (UAVs) for automatic visual inspection through an experimentation in real world. This method is under the assumption that the 3D model of a target facility, required accuracy of gathering data and specs of instruments are given. The triangular polygons for shape description of the 3D model are reconstructed for measurement and the measuring positions: the waypoints that an UAV travels to gather the accurate data of the target facility efficiently are calculated systematically. For basic evaluation of this method, we conducted an experimentation in real world. The measuring positions were calculated for an actual target and a camera. An experimenter brought a camera to the measuring positions and gathered image data. This experimentation result showed the effectivity of the method.

To realize automatic inspections for infrastructure facilities by an Unmanned Aerial Vehicle (UAV), improvement of localization performance is needed. Especially, automatic flight does not work in environments near facilities such as under the bridge because GNSS signals does not reach the UAV. We research a localization system using subsidiary UAVs that assist localization of an inspection UAV (main UAV). In this system, the positioning of the subsidiary UAVs is important to correctly complement the position information of the main UAV. In this paper, as the first stage of research, we consider a path of a single subsidiary UAV using preliminary information: the 3D geometrical data of a target facility, the inspection path of the main UAV, and the arrangement of GNSS satellites during the inspection. A simple simulation shows that the localization of the main UAV does not collapse through the whole path under the evaluation of the GNSS positioning precision of each UAV.

A new flexible fabric actuator realizing three dimensional movements for wearable devices is proposed in this paper. Such actuators must be lightweight and highly flexible, producing movements with high degree of freedom to assist/follow human natural motions. Improving the structure and control system of a fabric actuator from the previous research, the flexible fabric actuator with continuous control is developed. This paper presents new configuration of thin artificial muscles on a flexible rubber swath, a continuous control system to control the fabric actuator smoothly, and control methods to realize six basic movements of human body (forward and backward bends, left and right bends, left and right twists)with less number of muscles. The experiment results indicate the possibility that the proposed fabric actuator woven thin McKibben artificial muscles is a viable technology for use in wearable devices.

Wearable assist robots deliver power in form of direct contacts with the user, therefore, safety control is considered to be the prior requirement for wearable assist robots. To improve safety, the robot needs to be controlled based on the current contact state feedback. As a state estimation concept approach, a feedback control system based on joint torque has been proposed. The principle of this approach is that the physical contact forces appear as a certain change in measured joint torque. However, as the joint torque is the resultant of contact forces, the case of user is in danger contact state may not be detected. In this paper, a new control system based on pressure distribution is proposed. Utilizing the pressure sensors directly mounted on the robot's link surface, the robot is controlled to suppress excessive contact forces. On computer simulations, the proposed system was confirmed to be effective to improve safety.

This paper presents a measuring position determination method of Unmanned Aerial Vehicles (UAVs) for automatic visual inspection. Distinguishing measurable geometry of a target facility by an UAV and ensuring reliability in data gathering to detect anomaly are both important functions for inspections. The proposed method is based on the premise that the 3D geometrical data of a target facility and feature of an UAV and a sensor are given. The measuring positions to gather the visual data ensuring reliability are calculated considering positioning error of an UAV systematically. By setting the measuring positions as waypoints, flight paths appropriating for visual inspection can be obtained by using conventional path-planning method. The proposed method is examined through computer simulations on the assumption that an UAV measure image data of several bridges in an actual environmental.

The authors have researched on a method of human action modeling to realize systems such as to support human human operations or watch persons to prevent various kinds of accidents. In order to recognize or support various kinds of human actions, a certain human action model is necessary in these systems. Therefore, we have proposed a modeling method of human actions, which is extracted statistically from enormous data acquired from various kinds of sensors by long-term observation of human actions and situations around persons. However, human action elements composing a model should have been extracted heuristically by a designer. In this paper, an extraction method of human action elements is proposed in order to extract frequent human action elements automatically from acquired data. In the proposed method, a series of acquired time series data is divided into short partial ones, which are modeled by Hidden Markov Models(HMM). Then, the transition network of generated HMMs is constructed based on the likelihood between the original data and each HMM. In the obtained network, a transition sequence with the only one edge is regarded as a frequent human action element. Extraction results with artificial and actual human action data are shown in this paper in order to verify the usefulness of the proposed method.

This paper concerns the sensor data fusion method in mobile robot localization. Sensor data fusion methods using particle filter have been a major technique to estimate robots's state from noisy sensor observation. Generally, the sensors assumed to be in the nominal state of work. In realistic contexts, however, sensor characteristics may change online depending on sensor functioning conditions, and deteriorate estimation accuracy. To generate robustness to disturbance, faulty sensors should be identified and the fusion rule should be updated accordingly. These processes should be done online without access to ground truth, known as Fault Detection and Isolation (FDI) problem. The major approach to FDI problem in mobile robot localization has been the model-based online monitoring of the sensor state utilizing sensor fault models. However, comprehensive theoretical models for complex systems are difficult to obtain, and in some situation impossible to derive. Therefore, authors proposed a new sensor data fusion method for mobile robot localization adopting the idea of majority voting FDI techniques, which has been a proven technology in the field of safety critical control, utilizing the increasing and diversifying onboard sensors thanks to the technological development in recent years. Under the strong assumption of majority voting that the majority of similar signals provide the truth and any dissimilar signal is the result of a sensor fault, faulty probability distributions computed from sensor observations by a particle filter are detected and isolated utilizing K-L Divergence, a measurement for distance between two distributions in information theory. The proposed method is examined on a computer simulation, showing the possibility of generating robustness to the sensor data fusion in the mobile robot localization.

In this paper, a new impedance control method for wearable assist robots based 011 pressure distribution is proposed. Conventional power assist systems could not realize high power assist because they did not consider about the physical contacts between the user and robot. Also, these systems assisted only for simple joints such as knee, elbow, limb and so on. I11 order to achieve higher power assist lor multiple joints such as shoulder and waist, it is desirable to consider the contact condition directly instead of torque that was used in conventional methods. The authors propose a position based impedance control system using pressure distribution between the user and robot. By using a pseudo torque calculated by pressure distribution, the impedance controller can be expected to reduce the dangerous contact pressure efficiently. I11 this paper, power assist 011 the multiple joints are assumed: forward-backward bending movements of the upper half part of the body. A structure of the assist robot is determined based 011 analysis of human movements. By applying determined robot model 011 simulations, the proposed impedance control method is evaluated by comparing contact pressure to conventional methods.

We suggest motion adaptation method against environmental changes for service robots. Several motions such as walking, dancing, demonstration and so on are described with time series patterns. These motions are optimized with the architecture of the robot and under certain surrounding environment. Under unknown environment, robots cannot accomplish their tasks. We propose motion generation system based on heuristic search with histories of internal sensor values. New motion patterns are explored under unknown environment based on self-evaluation function. Self-evaluation function is composed of difference of internal sensor values between standard environment and unknown environment. New motion patterns are generated to maximize self-evaluation function without external information, such as run length, position of robot, human observation and so on. Experimental results show that the possibility to adapt autonomously patterned motions to environmental changes.

Robot motions are designed according to the properties such as the number of joints, the range of each joint, the length of each frame and so on. Structure changes such as joint failures or frame distortion make the tasks of prepared motions unaccomplished. Identification of structure changes on multi degree-of-freedom (DOF) robots like humanoid is so difficult that the traditional approaches based on inverse kinematics would not be applied. We propose a motion adaptation method to generate new proper motions accomplishing the tasks on changed structure without identification of the structure changes. All the motions can be expressed with the time-series data of postures. Each proper posture as an element of motions for changed structure is estimated by Kriging with joint angle data of several original postures sorted by Vector Quantization (VQ) and the corresponding postures explored on changed structure by Simulated Annealing (SA). At the case the tasks couldn't be accomplished with the generated motions in designed DOF, new motions are generated in expanded DOF adding redundancy joints. Experimental results show that the generated motion by proposed method is accomplished 96.6% similarity with the original task referenced failure motion's task to 0% on changed structure by single joint locking.