In recent years, kinesthetic illusion has gained attention as a means of presenting a sense of movement to users and improving immersion in human-machine systems for entertainment, skill acquisition training, and rehabilitation. Kinesthetic illusion refers to the phenomenon in which a stationary body part is perceived to move when vibratory stimulation is applied to muscles. However, the intensity of the illusion varies among individuals and some people do not perceive the illusions. To address this issue, this study proposes a method to induce and enhance kinesthetic illusions in the wrist joint by combining finger joint extension with vibratory stimulation. Finger joint extension is closely associated with wrist movements in daily activities. Therefore, by simultaneously presenting finger joint extension with vibratory stimulation, this approach aims to evoke the user’s motor imagery and strengthen the illusion. A psychophysical experiment confirmed that the proposed method induced stronger illusions than the vibratory stimulation alone. Furthermore, this study revealed that the angle to which the fingers are extended is a key parameter for enhancing the illusion using the proposed method.

A remote operation system for inexperienced tractor drivers is expected to enhance labor efficiency in agricultural work. This study aims to develop a method for evaluating tractor operators’ aptitude for remote operation by analyzing physiological signals and operation logs related to tension fluctuation. Experimental driving courses were designed to induce and assess tension fluctuations, and participants’ physiological responses were measured during these tasks. The experimental results have revealed that driving tasks designed in this study can fluctuate operators’ levels of tension. Moreover, the effectiveness of proposed indices based on task results and the evaluation of operators’ tension through physiological measurement was demonstrated.

The existing manufacturing systems based on processes involving the transportation of workpiece are unsuitable for large products such as air mobility systems. This study proposes a novel ultra-complex manufacturing system called the “Modularized-Structure and Multiple-Points Simultaneous Machining System (MS-cubic)” based on the concept of intelligent space, which simultaneously performs multiple types of machining processes without moving a workpiece. The system can simultaneously process multiple points and flexibly change its workspace by modularizing its structure. This paper presents a discussion on the requirements and constraints to generate a feasible design of the rail module and the machining unit, which are two main elements of MS-cubic. The performance of the prototype MS-cubic is evaluated, and its stiffness is observed to be sufficient to perform drilling. Furthermore, the modularized design of system enables the fluid and electric power supply for the machining process.

Unmanned robots have been proposed for the decommissioning of Fukushima Dai-ichi Nuclear Power Plant. To achieve efficient movement of robots in the high-radiation environment, we propose an “automated construction system of a modularized rail structure.” In the high-radiation environment, the rail structure must be constructed by a remotely controlled robot using minimal sensors. In addition, a compliant mechanism that allows small misalignments is not feasible in the module connection task due to multiple load conditions. Therefore, this study aims to achieve stable, sensor-less, and compliance-less construction using a remotely controlled robot. To achieve this goal, a geometrical model of a connection mechanism is generated and used for contact analysis of the kinematic chain transition. An analysis of the relative angle and distance between the connection surfaces of the modules effectively illustrates the conditions of connection success and failure. Based on the analysis results, the design is modified to stabilize the connection task and employed to update the constructor robot. Thus, the stability of the module connection is improved, even under various load conditions.

Ceiling mobile robots are anticipated for trans-porting component parts in production sites. In our previous study, we had developed a crawler-type ceiling mobile robot named "HanGrawler." In this study, we aim to realize 1 m/s and 90°/s speed movement on par with existing ground carriers, and reveal the factors contributing to the improvement in speed and stabilization. Accordingly, we develop HanGrawler 2, which inherits the basic approach of HanGrawler. In HanGrawler 2, the mechanism for hanging from the ceiling moves faster, and the driving power of the linear and rotational movements is improved drastically. Newly installed sensors detect the hanging mechanism and robot body position relative to the ceiling plate. By calculating the insertion timing based on the position of the hanging mechanism, the certainty of insertion is improved. Support rollers pressed against the ceiling stabilize the traveling posture of HanGrawler 2. Performance evaluation experiments confirm that HanGrawler 2 can travel linearly at a speed of 1 m/s with a 40 kg load and turn at a rotational speed of 90°/s. Based on the experimental results, it is confirmed that the design solutions for HanGrawler 2 are effective in realizing reliable ceiling mobility and that the high-responsiveness inherited from the original HanGrawler mechanism contributes to the realization of high-speed movement.

In this article, we present a ceiling-mounted mobile robot, HanGrawler, with high-speed mobility and the ability to freely select and adjust its route under a ceiling plate. HanGrawler hangs from the holes of a perforated metal ceiling plate using newly developed mechanically constrained hanging mechanisms mounted on crawler-type traveling equipment. These mechanisms continuously grasp the ceiling holes while moving in a straight line. Specially designed turntables are installed at certain ceiling points to change the HanGrawler movement direction to facilitate two-dimensional motion. A performance evaluation experiment confirms that HanGrawler can travel linearly at 0.1 m/s, turn at an 8.5°/s rotational speed, and carry a maximum load of 60 kg. The experiment also reveals that HanGrawler can carry a heavy load only when the timings of the hanging mechanism hooking motions and turntable rotational speed are tuned. This transport system can be utilized not only for part transportation on production lines but also for logistics in general.

In this paper, we propose a control algorithm to collectively transport an object using a group of relatively low-cost robots. We address this problem using the robust caging, which features reliable object closure with minimum number of robots, and requires no high-precision control capability on the individual robot. Given a 2-D convex object, the proposed method uses the quality of complete robustness to first optimize the number of robots in the initial formation, and then reorient and move the formation. The method is free of force analysis, and therefore less prone to sensor errors and failures. Compared with state-of-the-art multirobot object transport approaches, which require more robots and rely heavily on high-precision control, such as force and torque feedback control, our method uses fewer robots and has high tolerance to control noises. We performed both simulation and real-time experiments to demonstrate the performance of our method. We conclude that the proposed robust caging is promising under reduced number of robots and a certain level of control noises in multirobot object transport tasks.

While working, people often perform purposeless actions, such as spinning pens and swinging their feet. The pleasant and addictive tactile feedback generated by those actions seems to trigger them. This is based on a principle called "operant conditioning." "Operant conditioning" is a phenomenon of change in people's behavior because of a reward obtained from the purposeless action. In previous studies, operant conditioning for the behaviors of animals such as mice have been discussed, but conditioning for the behaviors of humans has had little investigation. Therefore, we designed a feedback for people's action as a reward of conditioning, especially tactile stimulation, and verified whether the stimuli can induce a simple action. We selected "tactile apparent motion" as the reward because it can provide comfort by stimulating a mechanoreceptor called C tactile afferent. We designed a wearable device that can generate tactile apparent motion from a simple input action, such as tapping the heel. Using this device, we conducted user studies. The results showed that fast tactile apparent motion is effective in inducing simple actions repeatedly. Moreover, simple actions can be induced by tactile apparent motion even in the long term.

In modern society, chronic stress in the workplace is a serious problem because it causes numerous diseases. To prevent chronic stress, it is necessary to continuously measurement this type of stress in the workplace. Thus, it is necessary to realize a chronic stress estimation method with small load to users. Therefore, we proposed a method to estimate chronic stress level by measuring changes of body motion patterns during desk work caused by chronic stress. For the acquisition of body motion patterns we called "Motion Pattern", a cushion-type sensor device with six pressure sensors was developed, and the changes of seat pressure distribution during desk work were measured. "Motion Pattern" was the time-series pattern of four"Motion Labels" including "Impact Label" which represented the motion that impacts the seat. We tried to estimate chronic stress level by learning the relationship between this "Motion Pattern" and the score of stress evaluation questionnaire. As a result, it was found that there was a significant difference in the interval time of "Impact Label" occurrence in all six participants. In addition, we achieved to estimate the existence and non-existence of chronic stress with an average accuracy of 87.0% using a classifier learned for each individual and achieved to estimate chronic stress level with a maximum accuracy of 70.0%for one participant. It suggests that our proposed method has potential for chronic stress monitoring in the workplace.

This paper presents an efficient algorithm to test whether a planar object can be caged by a formation of point agents (point fingertips or point mobile robots). The algorithm is based on a space mapping between the 2-D work space (W space) and the 3-D configuration space (C space) of the given agent formation. When performing caging test on a planar object, the algorithm looks up the space mapping to recover the C space of the given agent formation, labels the recovered C space, and counts the number of labeled surfaces to judge the success of caging. The algorithm is able to work with various planar shapes, including objects with convex boundaries, concave boundaries, or holes. It can also respond quickly to varying agent formations and different object shapes. Experiments and analysis on different objects and fingertip formations demonstrate the completeness, robustness, and efficiency of our proposal.

Crop grow measurement technologies are important to increase the farm productivity. Detection and measurement of fruit volume are useful for forecasting and harvesting applications. Some environmental challenges such as lighting conditions or occlusions make the fruit detection difficult. Our approach is based on features extraction from images through a sub-image clustering technique. Then images being described as a number of pixel in various labels are used in a regression model to estimate the fruit volume. The validity of the proposed method in experimental condition is successfully verified. The method is evaluated also in a field condition but results were inferior to the expectation. This paper tries to elucidate the reasons of the insufficient performance and tries to improve the proposed method in terms of illumination condition, precision and calculation time.

The goal of this paper is to develop a regrasp planning algorithm general enough to perform statistical analysis with thousands of experiments and arbitrary mesh models. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. We improve the pick-and-place regrasp approach developed in 1990s and analyze its performance in robotic assembly with different work surfaces in the workcell. Our algorithm will automatically compute the stable placements of an object, find several force-closure grasps, generate a graph of regrasp actions, and search for regrasp sequences. We demonstrate the advantages of our algorithm with various mesh models and use the algorithm to evaluate the completeness, the cost and the length of regrasp sequences with different mesh models and different assembly tasks in the presence of different work surfaces. Our results show that spare work surfaces are beneficial to assembly. Tilted work surfaces are only sometimes beneficial, depending on the objects.

Recently, there are an increasing number of developing wearable devices that intent to display various contents. As can be seen in sign languages and hand signals, hand gestures can express rich information with small motions, therefore, they are useful input for those devices. Especially, these gestures are less obtrusive actions than other actions such as speaking. It means that hand-gesture-based input interfaces are very suitable for public use, where a user can't use desktop devices such as a mouse or a keyboard. Although there are many studies on recognition of hand gestures, existing hand shape recognition methods have several problems to be applied for home automation. A wearable wrist contour sensor device has been proposed in previous studies. However discussions about how to design the protocols of hand gesture interface are insufficient. In this paper, we develop real time hand gesture interfaces of content browse for wearable or remote displays. Usability test using the developed interfaces reveals that pronation is to be assigned as a variable configurator and hand shape is to be assigned as an operation switcher.

In order to perform productive autonomous excavation of a fragmented rock pile, it is necessary to recognize the condition of the fragmented rock pile and to plan appropriate excavating motion according to the condition of the fragmented rock pile. In this paper, we propose imitation-based motion planning method, and develop a recognizer of rock pile condition and an excavating motion planner. We also develop an 1/10-scale excavation model and conduct excavation experiment. In the experiment, the proposed method works well from the view point of productivity. It is confirmed that the fragmented rock pile condition can be described by its shape and the particle size distribution of its surface. The proposed approach has feasibility in autonomous excavation of the fragmented rock pile. Experimental results also reveal that both the number of learning data and the diversity of learning data are important to realize a high-productive excavation.

“Grasping by caging” has been considered as a powerful tool to deal with uncertainty. In this paper, we continue to explore into “grasping by caging” and propose a new solution by using eigen-shapes and space mapping. For one thing, eigen-shapes fix dexterous hands into a series of finger formations and help to reduce dimensionality and computational complexity. For the other, space mapping builds a mapping between rasterized grids in 2-D Work space (W space) and rasterized voxels in 3-D Configuration space (C space) and helps to rapidly reconstruct C space so that we can efficiently measure the robustness of caging and find an optimal caging configuration for grasping. Our algorithm can work rapidly and squeezingly cage any 2-D shapes, including objects with either convex boundaries, concave boundaries, 1-order or high-order boundaries and even objects with inner holes. We implement the algorithm with MATLAB and carry out experiments with WEBOTS simulation to test its robustness to uncertainties. The results show that our algorithm can work well with various object shapes and can be robust to noisy control and noisy perception. It is promising in the power grasping tasks of dexterous hands.

In spite of IT innovation, people cannot get rid of non-creative tasks of searching daily-use objects at home. This paper presents a drawer-type storage system for supporting object search, “TansuBot”. By using this system and a smart device (e.g. smart phone), a user can review the photos of contents stored in the system. In addition, the system can present candidate drawers where the searching target object may be stored based on preliminary information such as usage histories. Concretely, LED blinking and pop-up actions (pushing drawers forward) are used for display. To realize these supports, a stacker crane type wall-moving robot is equipped at the backside of storage. The robot has a movable camera and mechanisms to push a drawer forward. For easy installation to a home, storage efficiency and cost reduction should be considered in the design of the instrument. Especially for cost reduction, this paper presents an approach to use wooden parts for main mechanisms. This approach also contributes to user-friendly presence and appearance of the instrument. This paper reports about the development of a prototype and an experiment to evaluate the functions for supporting object search. The results of the experiment prove the importance of the functions realized by the system; displaying contents' photos on a smart device and showing candidate drawers to investigate. The outcomes indicate that those functions have positive effects on reduction of searching time and mental burden.

Caging can offer robustness to uncertainties in grasping. If a robotic hand is designed based on the idea of caging, it would probably work well with noisy perception devices and low-quality control. This paper takes into account these merits and designs and implements a gripping hand based on the idea of caging. The gripping hand is concise and offers a low-cost alternative to co-operate with noisy data and low-quality control. According to previous work, we need four fingers to cage any 2D objects. That is to say, if each finger has one, two or three degree of freedoms, we will totally need four, eight or twelve actuators. The large number of actuators would be costly. This paper simplify the number of actuators into one by quantitatively analyzing finger formations with caging tests conducted on both random objects and objects from MPEG-7 shape database. It successfully lowers costs while maintains high performance. Following the simplified one-actuator design we implement a gripping hand by modifying a SCHUNK RH707 hand and carried out experiments with a manipulator built on the Neuronics Katana arm. The one-actuator gripping hand could work well with common depth cameras (Swiss Ranger) and pick up various objects. It bridges the gap between caging theories and applications and demonstrates the merits of caging.

Scores of health state for elderly people are regarded as important in nursing or medical fields. On the other hand, gaining the scores needs nurses to execute questionnaires. Owing to this, the execution rate for the health assessment is still low in ordinary homes. To solve this problem, we propose a method to predict the health score by using low-invasive sensors. We adopt regression as the prediction method and construct features to absorb the individual difference. As a part of feasibility study of social participation for elderly people, we execute the survey of health state using questionnaires by a nurse and install low-invasive sensors in real life simultaneously. Experimental result in the feasibility study shows a promise of the score prediction from sensor data. In addition, the result suggests that the extraction of features related to living behaviors improves the accuracy compared to using raw sensor data.

This paper presents a caging approach which deals with planar boundary clouds collected from a laser scanner. Given the boundary clouds of a target object and two fixed finger positions, our aim is to find potential third finger positions that can prevent target from escaping into infinity. The major challenge in working with boundary clouds lies in their uncertainty in geometric model fitting and the failure of critical orientations. In this paper, we track canonical motions according to the rotational intersection of Configuration space fingers and rasterize Work space with grids to compute the third caging positions. Our approach can generate the capture region with max(O(np),O(h2)) ≤ O(n2) cost where n denotes the resolution of grid rasterization, p denotes the resolution of canonical rasterization and h denotes the resolution of boundary rasterization or the number of boundary cloud points. Moreover, we propose a rough approximation which measures a subset of the possible positions by contracting rotations, indicating computational complexity of max(O(n),O(h2)). In the experimental part, our proposal is compared with state-of-the-art works and applied to many other objects. The approach makes caging fast and effective.

This paper presents a novel approach to deal with uncertainty in grasping. The basic idea is to initiate a caging manipulation state and then shrink fingers into immobilization to perform a practical grasping. Thanks to flexibility from caging, this procedure is intrinsically safe and gains tolerance towards uncertainty. Besides, we demonstrate that the minimum caging is immobilization and consequently propose using three or four fingers to manipulate planar convex objects in a grasping-by-caging way. Experimental results with physical simulation show the robustness and efficacy of our approach. We expect its leading benefits in saving finger number, conquering low-friction materials and especially, dealing with pose/shape uncertainty.

In this paper, we describe a novel sensor device which recognizes hand shapes using wrist contours. Although hand shapes can express various meanings with small gestures, utilization of hand shapes as an interface is rare in domestic use. That is because a concise recognition method has not been established. To recognize hand shapes anywhere with no stress on the user, we developed a wearable wrist contour sensor device and a recognition system. In the system, features, such as sum of gaps, were extracted from wrist contours. We conducted a classification test of eight hand shapes, and realized approximately 70% classification rate.

This research aims to develop a ceiling mobile robot that can sustain a large payload and can locomote freely under a ceiling space. In our approach, a perforated metal, one of the recent popular architectural materials, is utilized as a ceiling plate and the robot hooks and hangs the sequential holes of the ceiling plate by mechanical constraint. To realize the robot, the authors developed three key mechanisms, (1) ceiling hanging mechanism for the perforated metal, (2) horizontal locomotion mechanism like an inchworm, and (3) pantograph mechanism for smoothing horizontal locomotion speed and for load balancing. A unit testing of the ceiling hanging mechanism and the integration horizontal locomotion experiments confirmed the good performance of each mechanisms and the feasibility of mechanically constrained ceiling mobile robot. However, it was revealed that higher power motors and a stiffer body structure should be installed to sustain an extremely large payload like a human.

In this paper we propose a novel method for predicting resident's behaviors in a house from one's movement trajectories. The method consists of 1) segmentation of trajectory data into staying or moving and classification of the segments and 2) prediction by time-series association rules from transition events of each segment. The method predicts the start time of target behaviors for daily life support, such as eating, taking a bath etc. The time lag between the prediction and the target behavior can be set up manually, thus the method is adaptable to a variety of supporting systems. The experimental results using real residents' trajectory data of almost two years demonstrate that prediction of behaviors by the proposed method is feasible.

This research aims to realize a container case hand-over task between robots. Although sophisticated cooperative control is essential to avoid destructive internal force in general cooperative transfer task, we propose a geometrical caging strategy which can simplify a hand-over task. To solve a problem of capture region mismatch during caging state transition, the proposed strategy does not utilize additional actuators and sensors but a compliant mechanism whose displacement range can be changed automatically by its configuration. By experiments, it was confirmed that the caging strategy can make the hand-over task drastically easy and robust, and a winch type quasi-compliant mechanism is effective to the problem of capture region mismatch.

Laser based environment recognition technologies have been developed recently. Especially moving objects detection and classification by laser scanners mounted on a mobility is required for mobile robots and autonomous cars. In this paper, we propose a moving objects detection and classification method based on grid trajectories acquired from sequential laser scan data. Grid trajectories are obtained by voting sequential laser scan points on a grid map, and these trajectories not only work for a correct scan segmentation, but also represent the size and the speed of moving objects. We classify a moving object into either a person, a group of people, a bike, a car based on its grid trajectory. In our experiments, our mobility mounted laser scanners acquired scan data in the university campus, and the experimental results illustrate the effectiveness of the proposed method in outdoor environments.

This paper describes development of home-use automated container storage/retrieval system, a sub-system of a logistical support robot system in living space. The s/r system has 3 features. (1) Elevator type structure enables not only auto stocking motion, but also human access to containers as if the auto rack is a general shelf. (2) A separated motion layout enables us to utilize upper ceiling space as a stock space, and decreases possibility of accidental stuck by reducing the volume of robotpsilas motion. (3) The collaboration of a container guide plate and sensing with RFID actualize to navigate people to place a container on a shelf with desired position and posture. By experiments, it is confirmed that the ACSRS can transfer a container and 5 [kg] load (total 8 [kg]). We summarize significant technical points to design a system of this type.

This paper reports development of an intelligent container, a unit of the logistical support robot system in living space. The container not only supports human to fix and arrange their commodities, but also supports robots to achieve their logistical tasks. In other words, the intelligent container is a mediator between human's request and robots' capabilities. The container has four major roles in our logistical support system. (1) Taking several commodities in and recognizing and acquiring contents' information. (2) Supporting human and robots to transfer the container. (3) Receiving users' command as a simple user-interface. (4) Communicating to a host computer and informs the contents of the container and users' command. To realize those roles, the container is equipped with a grasping navigation mechanism for robot handling, RFID reader for the recognition of contents, and LCD device to display supporting information to human and robot. Finally, a primary function is evaluated in experiment to confirm that an acceleration sensor enables the container to detect being transferred by human. In the future, the intelligent container will play a role of contact point for human and robots in our target system, that enables human-robot symbiotic life.

This paper presents a ceiling mobile robot platform that enables multiple robots to move with low possibility of interference with humans and obstacles in an intelligent environment. An advantage of this platform is the potential to reduce sharing space between humans and robots while maintaining the robots' access to humans when their supports are needed. To realize the platform, two key techniques are developed. The first one is permanent magnet inductive traction method for the robots hanging under the ceiling plate. The second one is multiple robots simultaneous position measurement method using matrix of two dimensional codes. Validity of permanent magnet inductive traction method is confirmed by an experiment using a mock-up ceiling plate. Repeatability of position estimation using matrix of two dimensional codes is demonstrated by evaluation experiment. Finally as a demonstration of human support by the platform, transportation task is successfully performed.

In this paper, a novel describing method of human locomotion trajectory record was proposed, and methods of its application to the path planning and control of human-symbiotic mobile robots were indicated. The novel describing method, named as Existence Record Probability Map (abbreviated ERPM hereafter), was generated through the following three steps: 1) measuring human locomotion path using pressure distribution sensor floor, 2) applying a probability potential model of human body to the measured path, and 3) integrating such probability potential maps for some time period. From the ERPM, an existence probability value, ridge lines of the potential, and the Q-value at a point on a ridge line, were elicited. As a usage of these information, methods of finding a position where a collision probability of a mobile robot and a human is high, finding major trajectories of human locomotion, and evaluation of path width to avoid collision with a human, were indicated.

This paper proposes a high resolution sensor floor which can detect both humans and robots simultaneously. Each sensor floor unit is 500mm square and is equipped with 4,096 pressure switches distributed in a 64 /spl times/ 64 array. A 2m by 2m sensor floor with 16 of these sensor floor units has been realized. Experiments with this sensor floor have determined successfully the positions of a human and a 4-wheeled cart and can distinguish between them. The distinction is easily achieved because of the high resolution of the floor. The modular structure of the sensor floor enables easy application to a real room of irregular shape, allowing unconstrained measurement of the locations of humans, robots and objects in the room. Consequently, sensor floor systems will be essential components of future human-robot symbiosis systems to assist humans in daily life, and will also play important roles in medical and welfare robot systems.