The Future Robotics Center at Fukui University of Technology leverages the university’s advanced research achievements and technological expertise in robotics and automotive engineering. The Center actively engages in a wide range of challenges, from solving issues in local communities to contributing to global environmental improvements. This article introduces the Center’s research activities.

In recent years, most mowing work has been performed manually using a rotation grass cutter, which is a challenging task for workers, especially during the summer. Therefore, this study designed a system that can reduce their workload. A flail-type mowing system capable of mowing dense grass areas was designed. The performance of the proposed mowing system was experimentally evaluated.

The Moon’s interior holds valuable information for space research, making lunar excavation essential. Various research institutes are studying lunar excavation robots, but challenges remain in improving the excavation speed and ensuring the attitude stability of the airframe. To address these issues, an “annular spiral crawler mechanism” is proposed in which the crawler is wrapped around the cylindrical fuselage in a spiral shape. Evaluations confirm that the proposed mechanism enhances the excavation speed while ensuring the stability of the airframe by canceling the reaction force of the drill.

The Advanced Intelligence & Robotics (AIBot) Research Center at Tamagawa University is dedicated to advancing human-centered robotics and AI through interdisciplinary collaboration. This special issue highlights key research initiatives across several domains, including human-robot collaboration, cognitive robotics, and human assistive systems. The Robotics Research Division explores symbol emergence for natural communication and applies virtual reality (VR) technologies to model human-robot-environment interactions, aiming to optimize cognitive feedback and user experience. The Brain-Inspired AI Division investigates methods to enhance psychological well-being and self-efficacy using VR-based feedback in assistive contexts. The Tamagawa Robot Challenge Project links university-level research with primary and secondary education by leveraging robotics competitions and practical learning tools. Together, these efforts showcase the commitment of the center to foster next-generation robotics that supports the physical, cognitive, and emotional aspects of human life. This issue brings together selected studies that reflect the ongoing contribution of the center to the international research community.

This work proposes an integration of a vision system for a service robot when its gripper holds an object. Based on the particular conditions of the problem, the solution is modular and allows one to use various options to extract features and classify data. Since the robot can move the object and has information about its position, the proposed solution takes advantage of this by applying preprocessing techniques to improve the performance of classifiers that can be considered weak. In addition to being able to classify the object, it is possible to infer the sequence of movements that it carries out using hidden Markov models (HMMs). The system was tested using a public dataset, the COIL-100, as well as with a dataset of real objects using the human support robot (HSR). The results show that the proposed vision system is able to work with a low number of shots in each class. Two HMM architectures are tested. In order to enhance classification by adding information from multiple perspectives, various criteria were analyzed. A simple model is built to integrate information and infer object movements. The system also has an next best view algorithm where different parameters are tested in order to improve both accuracy in the classification of the object and its pose, especially in objects that may be similar in several of their views. The system was tested using COIL-100 dataset and with real objects in common use and a HSR robot to take the real dataset. In general, using relatively few shots of each class and a plain computer, consistent results were obtained, requiring only 8.192×10^-3 MFLOPs for sequence processing using concatenated HMMs compared to 404.34 MFLOPs for CNN+LSTM.

Service robots, in contrast to industrial robots, are devices aimed to operate in the service sector in replacement of or collaboration with human performers—in particular, domestic service robots carry out daily household chores. Recently, their popularity has increased and the range and difficulty of the activities they can perform are reaching amazing performances. There have been proposed several systems that can generalize single tasks such as object manipulation or scene understanding but they still fail on complex task planning, i.e., when and where they can perform such tasks. In this work, we propose the use of expert systems as a core module to understand user commands, infer the task context, request missing information, and plan action where each step of the plan may consist of basic state machines to generalized deep-learning models.

Interactive service robots must communicate effectively with users when teaching work procedures and seeking assistance in the case of errors. The evaluation of interaction quality is crucial for developing and enhancing the interaction capabilities of service robots. However, evaluating the interaction quality in daily life scenarios is significantly challenging, primarily because of its subjective nature. In this study, we present a series of case studies on evaluating interaction quality in the context of guiding daily life activities. Based on the results of the case studies, we discuss the remaining challenges and provide insights into developing a reasonable and efficient evaluation of the interaction quality of robots in guiding human scenarios.

In systems where agents teach skills in VR or digital applications, the focus is often on enhancing skill acquisition efficiency. This study focused on self-efficacy, defined as the belief in one’s ability to succeed in a specific behavior. Despite its significance, discussions in prior research have been qualitative and relied on subjective participant reports. This study aimed to quantitatively investigate the impact of successful personal experiences, observation of others’ successes, and positive reinforcement on self-efficacy in motor skills tasks. It also explores factors such as difficulty adjusting, performance differences between observing others and oneself, and experience sequences, which are often overlooked in self-efficacy discussions. By leveraging VR, we implemented Kendama juggling, which facilitated the observation of others’ successes, provided difficulty levels, and measured quantitative performance. Experiments examined whether modifying conditions such as performance differences, experience sequence, difficulty adjustment, and the presence of rewards would improve self-efficacy. The results suggested that self-efficacy improves when others demonstrate slightly better skills when observing others’ experiences before their own and with rewards, while lowering difficulty levels does not contribute significantly. This study established a methodology for designing assistive systems to improve self-efficacy through quantitative analysis.

Currently, natural disasters occur frequently. Therefore, disaster response robots are expected to reach disaster areas and operate at dangerous sites as quickly as possible. Humanoid robots are considered promising disaster response robots for performing various tasks instead of humans at disaster sites. To deploy robots at these sites, parachute descent is considered because the roads to reach the destination are generally destroyed. However, during parachute descent, the impact on the robot is quite significant compared to falling when standing or walking. To achieve parachute descent, it is necessary to generate an impact-absorbing landing motion. This paper develops a landing impact simulator to demonstrate the landing motion of a one-legged robot equipped with a series elastic actuator (SEA). The SEA features necessary characteristics to reduce impact while executing appropriate motion. First, the impact on a one-legged robot during landing was analyzed using a drop test. Second, based on the assumption of the linear spring-damper model of the SEA, the model parameters, such as the spring and damping coefficients, were identified through an optimization method using the results of the impact experiment. Subsequently, a landing impact simulation of the robot was performed to evaluate the validity of the developed SEA model. Compared with the drop test of a one-legged robot, it is confirmed that the developed SEA model has sufficient performance to reproduce the behavior of the flexible joint during landing.

In recent years, educational-support robots that assist learners in learning have attracted much attention. We focus on learning situations with robots where learners learn by solving problems. Conventionally, robots have a predefined number of problems for a learner to solve (hereinafter referred to as “the number of solved problems”), making it difficult for the robot to respond to the desire of the learner to learn. This makes it difficult to realize long-term collaborative learning with robots; thus, it is important to create a learning environment in which learners can learn spontaneously and are motivated to learn. We propose a “problem-number negotiation method,” in which the learner decides the number of solved problems while conversing with the robot before the start of learning. For the robot to achieve an appropriate conversational tone in this method, a conversational tone based on politeness theory was adopted. The proposed method was compared with that of a conventional robot to determine whether the proposed method increases the number of solved problems. Results showed that junior high school students spontaneously solved more problems with the robot equipped with the proposed method than with the conventional robot during short-term learning.

The aim of this study is to clarify the characteristics of visually induced motion sickness (VIMS) during tele-operation of construction machinery using subjective and objective indicators. To clearly define and ensure the repeatability of the experiment conditions, we used virtual reality to simulate a real-world construction machinery tele-operation system. 90 people with no prior operating experience participated experiments under the experimental conditions of image presentation device and delay in reflecting operations in generated images. To evaluate visually induced motion sickness, we used body sway and the R-R interval, the ratio of low-frequency to high-frequency components (LF/HF), and the m and S indicators of Lorenz plots derived heart rate variability. In addition, subjective evaluation was performed using the simulator sickness questionnaire. We conducted a multifaceted analysis including interactions between time-series variation in operational performance and physiological indicators and subjective evaluations. In the operational performance results, the participants demonstrated reasonable learning effects while exhibiting negative effects of delays. The variation in body sway indicators among participants was too large to demonstrate the effects of operation delays. No effects of the experimental conditions were found when averaging the heart rate variability under the assumption that there is common time-series variation among the participants. A relation, however, was found between heart rate variability and the subjective symptom of sickness by using an indicator based on broader variation in heart rate variability. This is a finding that previous studies using similar experimental conditions for VIMS in tele-operation of construction machinery have not been able to demonstrate.

The effectiveness of PARO, animal-like robots, as robot-assisted therapy has shown a positive impact on non-pharmacological therapy. However, the effectiveness of caregivers presenting PARO to patients needs to be evaluated due to the different skills possessed by caregivers. The way caregivers present PARO to patients will impact the patients’ interest response towards PARO. We propose a system that can measure patients’ interest in PARO by recognizing the reciprocal attention between PARO and humans. We built the system by integrating human skeleton tracking and PARO’s posture detection. Human skeleton tracking is reconstructed from multiple Kinect Azure devices, while PARO’s posture detection is achieved using a single RGBD camera placed on top of the field. We extract the attention parameter by calculating the difference in gaze direction between PARO and humans. The results showed that the proposed method successfully detected the gaze interaction between PARO and humans with an average accuracy rate of 97.59%.

The role of robots has evolved with recent technological innovations and social challenges, such as declining workforce. Robots are no longer confined to the industrial field but are becoming an integral part of daily lives. Consequently, their optional environment has shifted from factories to human living space. As the presence of robots in these environments continues to grow, human-robot interaction has emerged as a critical issue that requires further exploration. Robotics competitions serve as a platform for testing solutions to these challenges, with many events focused on advancing robot integration into society. However, a competition specifically designed for human-robot coexistence has not yet been proposed. This study aims to present a vision for a future society where humans and robots coexist. To facilitate discussions on this concept, we organized a robotics competition in which humans, animals, and robots can participate. Accordingly, we designed a robotics competition incorporating humans, animals, and robots based on a relay race, known as Ekiden. Furthermore, we outlined the design principles of the competition, described its rules and results, and evaluated the validity of these rules.

This study proposes a data-driven optimal robust design methodology for a fractional-order proportional-integral-derivative (FOPID) controller. This methodology concurrently determines the parameters of the FOPID controller and reference model under a stability-margin constraint by utilizing only one-shot input-output data. The stability margin, which is quantitatively defined as the maximum value of the sensitivity function, is specified by the designer. This approach ensures a balanced design based on the trade-off between robust stability and servo or regulator performance at a given robust-stability level. Numerical examples substantiate the superiority of the FOPID controller over the conventional integer-order proportional-integral-derivative controller.

The high rate of lower back pain in workplaces that involve lifting heavy loads is a worldwide problem. Various lower back assistive devices have been developed to reduce the lower back burden during lifting work; however, they often have issues such as weight, workability with them and more, and are not used continuously in workplaces after purchasing. In this study, we conducted parameter adjustments of the pneumatic spring using a single-acting air cylinder to develop a device capable of assisting in lifting tasks. We performed requirements analysis, modeled lifting movements, examined various parameters of the assistance device, and designed skeletal structures and attachment components to meet the target weight.

This study presents a novel method for helping hemiplegic patients put on a long-sleeved shirt. Our analysis specifically focuses on the behaviors of patients with hemiplegia in the latter half of the dressing phase, which involves moving the garment to the healthy side after dressing the paralyzed side. We propose a novel method to assist this movement. A key aspect of the proposed method is the inclusion of a module capable of predicting the target position of a robot’s end effector using time-series depth images. By combining this module with force-based control, a single-armed robot helps a wearer manipulate their garment according to their movements. Experiments utilizing an articulated manipulator demonstrate the effectiveness of the proposed method.

This study presents a robot that efficiently picks up objects scattered on the ground, such as nuts in outdoor work. In conventional methods, objects are generally picked up by a gripper attached to the end of an arm; however, picking them up one by one is time-consuming. In addition, difficulties arise from the complexity required for coordinated control of the grasping posture and movement to pick up the object, as well as from the high rigidity of the machine when working on uneven ground. In this study, a cage-type end-effector is employed to enable continuous pick-up while the robot moves. In addition, using a worm gear for the arm, an arm-type mobile robot with a small size, simple drive source, and high mechanical safety is designed. To improve the pick-up capability, force control that allows the end-effector to flexibly follow the unevenness of the ground is desirable. However, force control is generally considered difficult because a worm gear cannot be driven in reverse. Therefore, we constructed a contact force control system that adjusts the arm angle by measuring the unevenness of the ground as spring displacement around the end-effector. This control system prevents excessive contact force between the end-effector and the ground. Experiments in an outdoor environment confirmed that the variation in the number of nuts picked up was reduced by approximately 48% compared to the case without the control. These results demonstrate the effectiveness of the pick-up system proposed in this study.

This study investigated an assembly strategy that leverages only a single robotic manipulator and an attached parallel gripper. This approach reduces the need for additional fixtures, such as manipulators and fixturing jigs, and minimizes costs and workspace footprint. We discuss the mechanical and geometric conditions required to improve the robustness of objects in the assembly process. The mechanical conditions represent the force equilibrium of multiple objects, and the geometric conditions restrict the movability of the objects. As some of the support jigs used in the experiments were not controlled to firmly fix the assembled objects, the above conditions were essential to guarantee the robustness of the temporarily assembled parts. This paper presents a successful case of automated assembly using a single manipulator, demonstrating the potential of the proposed method.

Recently, the damage caused by harmful animals to crops and to people’s homes has been increasing, and this has become a problem. As of FY2022, damage to crops caused by wild birds and beasts amounted to 15,563 million yen. To reduce damage, electric fences are often installed between forested areas, the habitat of harmful animals, and fields or people’s homes. Electric fences are effective when properly installed and maintained. However, they have disadvantages, such as the risk of electrical leakage if grass or trees come into contact with the wires, and cannot be used during the winter season, making them difficult to operate in uneven terrain. In addition, the labor and costs involved in installation are significant, and considering the recent shortage of agricultural workers in Japan, it is difficult to maintain the current status quo. As a countermeasure for this problem, the authors developed a simple and easy-to-use robot system that can be easily installed in mountain forests, using a moving mechanism with overhead wires and visual information. In particular, this study describes the development of software for detecting harmful animals using visual information. The developed software is characterized by its ability to detect harmful animals with higher accuracy by combining a motion detection algorithm and the object recognition model YOLOv5.

Intelligent rehabilitation aids achieve real-time tracking of patient treatment and personalized medical support by integrating sensing and detection technologies, data processing, and machine learning. In light of the lack of sensor monitoring systems in spinal orthosis therapy, this article systematically reviews the current research status of intelligent spinal orthosis monitoring systems. It focuses on discussing several key technologies for realizing the digital and intelligent technologies of spinal orthoses, such as distributed pressure sensing technology and pressure sensor software compensation algorithms. Finally, this article proposes a technical roadmap for developing an intelligent spinal orthosis monitoring system based on distributed pressure sensors. This solution provides theoretical support and technical implementation paths for the digitalization and intelligence of spinal corrective device treatment processes. It is expected to enhance the effectiveness of spinal scoliosis treatment and further promote the intelligent development of medical rehabilitation aids.

Mobile robot navigation plays a vital role in robotics, allowing autonomous systems to function effectively across different environments. Artificial markers offer a dependable and efficient solution for localization and mapping, especially in structured environments. This systematic mapping study aims to thoroughly examine the existing body of literature, including research papers that explore mobile robot navigation techniques utilizing artificial markers. Our review systematically analyzes various aspects such as marker types, detection algorithms, and navigation strategies. The study aims to uncover research gaps, highlight emerging trends, and suggest potential future directions. By evaluating the strengths and limitations of current approaches, we strive to address key research questions related to marker-based navigation systems for mobile robots.

Upon further review of this article, the authors noticed an error in Fig. 3 that they would like to correct. This error occurred unintentionally in the process of rebuttal. The correction does not change any of the conclusions.

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Fig. 3. Sensor location to identify stimulation positions and example of the force applied to the sole during manual therapy. Force was measured from three locations of the sole: (a) example of sensor locations to identify the stimulation positions, (b) longitudinal arch, (c) transverse arch, and (d) pollicis brevis muscle.

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Fig. 3. Sensor location to identify stimulation positions and example of the force applied to the sole during manual therapy. Force was measured from three locations of the sole: (a) example of sensor locations to identify the stimulation positions, (b) longitudinal arch, (c) transverse arch, and (d) pollicis brevis muscle.

The authors regret this error, and this error has now been corrected in the PDF version of the article.