The objective of this research was to realize human tactile exploration in a VR environment. For example, this capability would allow users to check the surface texture of products before purchasing them through online shopping or enhance the immersion of VR experiences. Therefore, this paper proposes a device that enables users to move their hands to trace virtual objects. This device provides tactile perception by tracing an image displayed on a screen using a cursor. We propose a soft vibration actuator mouse (SVA-M) that incorporates a haptic display into a computer mouse. The haptic display employs a soft vibration actuator (SVA). An SVA is a compact actuator that vibrates at high density, has a wide frequency range, and is capable of presenting high-resolution haptic feedback to the fingertip. We conducted a perception test to evaluate the spatial resolution of the haptic textures that the device could present. We investigate the spatial resolution of the haptic textures perceivable by humans by having participants trace six different haptic textures using the device. The results indicate that, under the reduced tracing speed imposed by device operation in a standard 60 Hz display environment, textures with haptic changes corresponding to hand movements of 0.16 mm or more can be reliably discriminated, establishing a criterion for haptic texture design in mouse-type haptic displays intended for everyday use.
This paper describes the Robotics Research Center at Ritsumeikan University. The center was established in 1994, following the opening of the Biwako–Kusatsu Campus of Ritsumeikan University. The Robotics Research Center aims at “society-driven, needs-based” research to solve problems in industry and society, based on fundamental research and system integration technology from the university. The center conducts a wide range of research on field robots, biorobotics, manufacturing robots, interactive robots, mechanisms, and devices.
Original article: Journal of Robotics and Mechatronics, Vol.38, No.1, pp. 329-340, 2024. doi: https://doi.org/10.20965/jrm.2026.p0329 Upon further review of this article, the authors noticed errors on page 333 that they would like to correct. This error occurred unintentionally during the unit conversion and manuscript preparation process. The correction does not change any of the conclusions. Originally published text Corrected text Section 4.1: In the current system, the rotational speeds of SM1 and SM2, which perform the twist operation on the precursor fiber, were maintained at 90 rpm, whereas the feed speed provided by SM3 was manually adjusted for tension control, as previously described. Section 4.1: In the current system, the rotational speeds of SM1 and SM2, which perform the twist operation on the precursor fiber, were maintained at 27 rpm, whereas the feed speed provided by SM3 was manually adjusted for tension control, as previously described. Section 4.3: In addition, while the previous report set the rotational speeds of SM1 and SM2 at 30 rpm, this study used 90 rpm to accommodate the production of longer TCPAs, which might have adversely affected the twisting process stability. Section 4.3: In addition, while the previous report set the rotational speeds of SM1 and SM2 at 9 rpm, this study used 27 rpm to accommodate the production of longer TCPAs, which might have adversely affected the twisting process stability. The authors regret this error, and this error has now been corrected in the PDF version of the article.
The most common life-threatening issue in heavy snowfall regions is human casualties caused by snow damage, particularly accidents during snow-removal operations, such as roof snow clearing. This study aims to develop a telescopic convex tape expansion mechanism to prevent such accidents. We constructed and evaluated a prototype telescopic mechanism (Makijaku-Ude Type-Super K) featuring a reduced number of parts and eliminating springs. The results confirm its ability to withstand stronger snow loads during operation. The robot system, named Setsu-ichi, equipped with two Type-SK units, successfully completed snow-removal operations over a distance of approximately 7 meters.
Food decoration usually requires a robotic system to perform multiple motions such as grasping, reorientation, and pushing. In this study, a novel gripper capable of grasping, caging, and pressing was developed with two-degrees-of-freedom featuring a parallel-drive mechanism and actively rotatable fingers. By employing two motors and two rack-and-pinion mechanisms, the system enables the simultaneous opening-closing and rotational motions of multiple fingers. This enables the grasping and caging of a target object. With the help of the inverted V-shaped fingers, the gripper can realize pressing motion after placement for stabilizing food decoration. To control the gripper, a kinematic model was derived to relate motor motions to finger motions. Experiments were conducted to decorate chestnuts on a dorayaki and the results revealed that a pressing motion is essential for stable decoration. Additionally, experiments on grasping various fragile food items demonstrated the effectiveness of the caging motion.
This study proposes a unit-assembly architecture for robot modules that enables tool-less disassembly and automated assembly as a design pathway for recyclable robots. The robot module is decomposed into nine separable functional units (drivetrain, cooling, and control groups) connected exclusively via three types of releasable interfaces: magnetic docking, magnetic dowels, and mortise-and-tenon joints. This architecture allows non-destructive disassembly of all functional units in eight pull or slide operations, enables the clean separation of material streams, and facilitates the recovery and reuse of high-value components (motors, sensors, thermoelectric coolers, and printed circuit boards). Experimental validation confirms that the design does not compromise performance: the drivetrain achieves stable positioning (settling time ∼0.9 s, steady-state error ∼1%) and the active cooling system reduces motor temperature to room temperature within 200 s. Furthermore, we demonstrate an automatic assembly system using a 4-DOF robot arm with YOLOv8-based vision (mAP50=0.828), thereby validating that a representative set of releasable interfaces supporting disassembly are also compatible with the robotic assembly. Full nine-unit automatic assembly remains a topic for future investigation.
Neurorobotics, which incorporates neuromorphic computing into robotic systems, can emulate biological intelligence and behaviors. The characteristics of neural responses and behavioral patterns are reproduced using spiking neural networks. Many applications utilize mobility robots; however, applications with multiple joints, such as robotic fish, remain limited. The main challenge is that shape types and joint numbers vary among biological organisms, and disturbances affect them in underwater environments. The neural circuit mechanisms used by multi-jointed robots for obstacle avoidance have not yet been clarified. In this study, we propose an obstacle avoidance architecture realized exclusively through neural circuits. The fundamental obstacle avoidance performance of a robotic fish was systematically evaluated, including ablation studies, varying conditions, and the physical environment.
In the field of nursing, hand massage has attracted attention because it can be easily performed anywhere and has numerous benefits, such as reducing anxiety and stress, promoting healing, improving health, and providing relaxation. However, mastering hand massage techniques requires instruction from a professional instructor. Challenges include the difficulty of self-study and of visually assessing the amount of force applied. To improve learning efficiency, this study develops a massage movement learning support system that visually displays force information in real time, allowing for objective evaluation. The learning system displays force information on an arm simulator model with a smooth surface, using a sheet-type rubber sensor and full-color LED lights. Basic experiments are conducted to evaluate the effectiveness of the developed learning system. In addition, a method is proposed for evaluating massage skill and learning level based on changes in force distribution detected by the rubber sensor. The effects of learning are verified using the proposed evaluation indices and physiological evaluations.
Lower-limb rehabilitation robots are valuable for gait training, but accurate joint motor angle tracking remains challenging due to various motion-related disturbances. This paper presents a staged joint-compensation strategy to improve control accuracy. The gait control process is partitioned into initiation, cyclic, and termination phases. A multilayer perceptron is employed during initiation and termination to predict and compensate for short-term aperiodic errors, while a Transformer-based sequence model combined with repetitive-control concepts is used in the cyclic phase to predict and correct periodic errors. Phase detection and safety-constraint mechanisms are integrated to ensure system stability and safety. Experiments are performed on a self-developed robotic platform with field-oriented control at the motor level, using a 165 cm, 60 kg dummy as the load. The proposed strategy substantially reduced joint-angle RMSE: left hip from 0.692° to 0.494° (28.6% reduction), right hip from 0.687° to 0.402° (41.5% reduction), left knee from 1.754° to 0.426° (75.7% reduction), and right knee from 1.667° to 0.461° (72.3% reduction). Ablation studies and repeated-trial statistical analyses further confirm the effectiveness of the approach. This study significantly reduces the gait trajectory tracking errors of joint actuators in a lower-limb rehabilitation robot, thereby providing a feasible and effective approach for the optimization of its control algorithm design.
High spatial resolution tactile sensors are essential for robots to perform delicate and highly accurate tasks. To achieve sub-millimeter-order high spatial resolution, a tactile image sensor using mechanical-optical materials was fabricated using strain-sensing polymers. Strain-sensing polymers are polymer materials whose light reflection wavelength characteristics change depending on the magnitude of the applied strain. This tactile image sensor has a structure in which a strain-sensing polymer sheet is placed on a transparent acrylic substrate, and the sheet is photographed from the back with a color camera. By mapping the hue value to the pressure at each pixel using the color image of the strain-sensing polymer acquired by the camera, the pressure distribution on the sensor surface was estimated with sub-millimeter-order high spatial resolution. The developed sensor was able to estimate the pressure distribution with a spatial resolution of at least 0.4 mm and a measurement range of up to approximately 0.7 MPa. Furthermore, experiments confirmed that the high spatial resolution of the proposed tactile image sensor is also effective in accurately estimating the orientation of the object being contacted. This sensor is useful for robot hand to estimate the gripping state of minute or thin objects and perform manipulation tasks with high precision.
Massage has both physical and mental benefits. Pressure massage is suitable for exerting a strong force on the skin’s surface in the normal direction. This technique is taught visually or using force feedback. Quantitative evaluation and learning methodologies are required. In this study, we propose a learning support system for massage techniques. The applied force and angle are quantitatively evaluated. Large forces, such as those applied during a massage, are difficult to measure using conventional rigid sensors or sensors incapable of handling large displacements. Therefore, we propose a flexible three-axis force sensor with an inflatable structure using pneumatics. The sensor is made of a non-elastic material; therefore, it can measure large forces based on volume changes. We show the design and construction of the proposed sensor and evaluate it.
This paper proposes a method to quantitatively evaluate human posture recovery performance when mechanical disturbances are applied during walking, considering that human falls mainly occur while walking. During walking, the reaction force from the soles of the feet acts as a disturbance during normal walking, which makes it difficult to distinguish between postural responses due to intentional mechanical disturbances and those due to normal walking. Furthermore, it is not easy to reproducibly apply mechanical disturbances during walking. The study uses a treadmill with independent left and right belts. The speed difference between the left and right belts is used as the disturbance input, and the dynamics of human postural correction due to disturbances is identified using system identification with the response of the center of pressure as the output. A method to quantitatively evaluate the posture recovery ability is proposed using the pole distribution of the identified dynamics model. The effectiveness of the proposed method is shown by experimentally demonstrating that there is a quantitatively significant difference in posture recovery performance when posture recovery ability is artificially reduced compared to when it is not reduced.
This study considers a merging scenario on a congested highway on-ramp and develops a system that autonomously performs lane changes from an on-ramp to the main lane. In particular, we focused on constructing a method for selecting a target space in a congested traffic flow into which the host vehicle enters itself. Driving-behavior data of human drivers were collected using a driving simulator, and the proposed method was constructed by mimicking human decision-making mechanisms through machine learning. This enables autonomous vehicles to make natural decisions that are similar to those of human drivers, thereby enhancing the social acceptance of autonomous driving technologies. Simulation experiments confirmed that the proposed method ensures sufficient safety while selecting a target space comparable to that chosen by human drivers.
In recent years, sample returns from celestial bodies have remarkably developed as important elements of space exploration. The targets are becoming increasingly distant celestial bodies. Improving mission flexibility and reducing risk are important in deep-space exploration. The deep-space orbital transfer vehicle concept, which separates the sampled child spacecraft from the parent spacecraft that navigates between celestial bodies, has been proposed as a solution. The key technology of such a system is the autonomous docking in deep space. The docking mechanism must have low resource requirement, high success rate, and low guidance control requirements. The authors propose a novel docking mechanism with high capacity for positional errors. The mechanism has claws that can cage a grapple fixture, after the initial contact, using spring force and then fix it by driving a motor. This paper presents the concepts, requirements, and design of such a docking mechanism. Subsequently, evaluation using a prototype model is discussed.
This study proposes a dual-purpose system for remote medical support during disasters and trauma treatment education, aiming to enhance procedural guidance and training effectiveness in resource-limited and high-risk environments. The system consists of a remote support system for trauma treatment and a medical education system for trauma training. The remote support system enables trauma experts at distant locations to interact with a 3D digital twin of disaster victims and their surroundings within a virtual reality environment, thereby providing procedural guidance to on-site medical personnel. The system uses widely available devices such as smartphones for 3D scanning and head-mounted displays for immersive visualization. This design enables rapid deployment at disaster sites without requiring specialized equipment or complex setup procedures. The trauma education system records expert surgical treatments using motion-capture technology and reconstructs them as interactive 3D avatars, allowing trainees to observe and learn techniques from multiple perspectives. The remote support system was evaluated through fingertip-based interaction tasks in a simulated disaster scenario, where the alignment accuracy was assessed using augmented reality overlays, resulting in a measured average error of 27.0 mm. Similarly, the trauma education system was evaluated for positional accuracy in instrument handling tasks. These results confirm the feasibility and practicality of the proposed system and demonstrate its potential to improve both emergency medical response and surgical education.
This study aims to enhance tactile sensing for practical robotic applications by enabling the acquisition of dynamic contact information in optical tactile sensors. Conventional optical tactile sensors measure displacement and torque with high precision by detecting the deformation of transparent flexible resin using photoreflectors; however, they do not fully exploit information from minute vibrations or dynamic contact events. In this work, we propose a lightweight, low-cost, and robust optical tactile sensor capable of texture recognition and slippage detection without relying on acceleration sensors or piezoelectric elements, offering a simpler and more durable alternative to conventional high-definition camera-based approaches.
In Japan, the quantity of domestically produced fruit has been gradually decreasing, while wholesale prices have continued to rise due to declining production volumes and a shift toward high-quality varieties. To address these trends, improving quality and reducing labor through automation have become urgent challenges. In precision viticulture, monitoring the growth of grape clusters plays a key role in yield estimation, disease management, and optimal harvest timing. Although recent advances in deep learning and 3D reconstruction have enabled accurate fruit detection and modeling in vineyards, tracking the same clusters on different days remains challenging because of branch movement, fruit growth, and varying imaging conditions. This study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters. Stable vine structures, such as trunks and main branches, are reconstructed using Structure from Motion, and their spatial correspondences are estimated through SIFT-based matching and similarity transformation. Once the coordinate systems of different days are aligned, the grape clusters detected by CenterNet are associated based on spatial proximity in the unified 3D space. Experiments over multiple observation days demonstrated that the proposed method successfully maintained the consistent tracking of grape clusters throughout the growth period. These results indicate that branch-based alignment effectively stabilizes multi-day observations and facilitates the temporal monitoring of fruit growth, supporting automated phenotyping and future field robot applications in viticulture.
Wearable assistive robots have been studied widely to reduce the physical burden of tasks such as lifting and caregiving. Most existing systems, however, control only the joint torque. Even though wearable robots transmit actuator forces through direct contact with the body, these systems do not focus on managing the contact force generated at the human-robot interface. Our previous study developed wearable assistive robots capable of measuring and controlling the contact-force distribution. We verified the feasibility of evaluating safety and control performance using a single-joint arm-mounted robot and a multi-joint whole-body robot. However, the influence of force distribution on assist performance was not been sufficiently examined for multi-joint robots under assist conditions. The current study aimed to experimentally verify the effectiveness of using contact-force distribution information in a torso-mounted multi-joint robot (through experiments involving seven participants). The results showed that whereas the actual motor torque tracked the commanded torque under conventional torque-based control, the contact-force distribution experienced by the wearer varied across participants. This finding indicates that for wearable robots with complex joint structures, assessments of wearing states and assist effectiveness require an evaluation of the contact-force distribution in addition to the torque.
The inherent nonlinear characteristics of friction adversely affects the control accuracy of joint motor drive systems in lower limb rehabilitation robots. Recognizing this challenge, this study proposes an improved friction model and further designs a feedforward compensation control scheme to mitigate motor friction on the basis of the friction model. Compensating for motor control utilizing the friction model, typically the Stribeck friction model, is a promising solution. To overcome the inherent limitation discontinuities of the Stribeck friction model, this study introduces an improved friction model by incorporating the sigmoid function into it. The friction parameter of the model is identified based on the data collected during the experiment, specifically the motor velocity and current. And to enhance the precision of the parameter identification, Kalman filtering algorithm is applied to mitigate the Gaussian noise generated during the experiment. Subsequently, the firefly algorithm is employed for offline identification and curve fitting of the friction parameters in the improved model. Based on the improved friction model, a feedforward compensation controller is further designed by integrating the traditional three-closed-loop PID motor control method with real-time friction compensation. The system employs humanoid gait patterns as input signals to achieve precise position tracking of the robot’s joint motors. Compared with the conventional PID control, the proposed feedforward compensation control reduces both position and current tracking errors. These results confirm that the feedforward compensation strategy, based on the refined Stribeck friction model, effectively mitigates the adverse effects of nonlinear friction, thereby improving the control performance of joint motor drive systems.
High-speed sensors, such as high-speed cameras and optical proximity sensors, enable the detailed temporal measurements of physical phenomena that exceed the dynamic capabilities of conventional industrial robots. However, effectively leveraging this sensor information for robot motion planning remains challenging because of the temporal-scale gap between sensors and robots. This paper proposes a motion planning method that extracts the task-relevant meta-information of target phenomena from high-speed sensor data and generates feasible trajectories by considering robot constraints. The information extraction process identifies task-relevant characteristics from high-speed sensor data. To integrate heterogeneous sensor information and enable trajectory adaptation, we employed multiple virtual-dynamics-based control (MVDC), which can asynchronously integrate heterogeneous sensors with different measurement principles. To validate the proposed method, we conducted a case study in which a conventional industrial manipulator grasped a pendulum at its equilibrium point, the most challenging position. The system integrated global measurements from a 1 kHz high-speed camera with local measurements from proximity sensors using MVDC to predict the pendulum period and optimal grasping timing. Experimental results demonstrated that the proposed method enables successful grasping by bridging the temporal-scale gap between high-speed sensors and conventional robots through information integration.
