In the preface of this special issue, we will discuss the achievements of the Toyohashi University of Technology and the Center for Human-Robot Symbiosis Research so far and the direction they should proceed in hereafter. In particular, the history of the establishment of the center, research results until now, future activity plans, and this special issue are described.

This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. The HGA algorithm is validated using the following three different fitness functions: the number of cell visits, traveling time, and a new energy fitness function based on experimentally acquired energy values of fundamental motions. Computational results show that compared to conventional methods, HGA improves paths up to 38.4%; moreover, HGAs have a consistent fitness for different starting positions in an environment. Furthermore, experimental results prove the validity of the fitness function.

The driving agents considered thus far have aimed at navigating the driver’s attention while driving, for example, using interactions through linguistic conversations. Therefore, in this study, to investigate such a role in automatic driving from the perspective of nonverbal communication focusing on physicality (e.g., head movements and eye gaze), we constructed a driving agent called NAMIDA, along with its physical properties, as a research platform to investigate the role of nonverbal communication. We conducted a cognitive experiment on attention guidance, focusing on “gaze direction,” i.e., the movement of the eyes of NAMIDA. As a result, we confirmed that the attention of the participants is attracted by such eye-gaze movements of “NAMIDA,” which become a “cue” to exploring the surroundings.

The authors have been developing a mobile robot to assist doctors in hospitals in managing medical tools and patient electronic medical records. The robot tracks behind a mobile medical worker while maintaining a constant distance from the worker. However, it was difficult to detect objects in the sensor’s invisible region, called occlusion. In this study, we propose a sensor fusion method to estimate the position of a robot tracking target indirectly by an inertial measurement unit (IMU) in addition to the direct measurement by an laser range finder (LRF) and develop a human tracking system to avoid occlusion by a mobile robot. Based on this, we perform detailed experimental verification of tracking a specified person to verify the validity of the proposed method.

The soft robot is a kind of continuum robot, which is mainly made of soft elastic material or malleable material. It can be continuously deformed in a limited space, and can obtain energy in large bending or high curvature distortion. It has obvious advantages such as high security of human-computer interaction, strong adaptability of unstructured environment, high driving efficiency, low maintenance cost, etc. It has wide application prospects in the fields of industrial production, defense military, medical rehabilitation, exploration, and so on. From the perspective of the bionic mechanism, this paper introduces the soft robots corresponding to insect crawling, snake crawling, fish swimming, elephant trunk, arm, etc. According to different driving modes, the soft robots can be classified into pneumatic-hydraulic driven, intelligent material driven, chemical reaction driven, and so on. The mechanical modeling, control strategy, material, and manufacturing methods of soft robot are summarized, and the application fields of soft robot are introduced. This paper analyzes the main challenges faced by the research on the key technologies of soft robots, summarizes and analyzes them, and puts forward the prospects for the future research of soft robots. The development trend of the future is to develop the soft robot with the characteristics of micro-scale, rigid-flexible coupling, variable stiffness, multi-functional, high integration, and intelligence of driving sensor control.

In nursing homes, repeatedly guiding several carereceivers in wheelchairs before and after meals is one of the factors that increase the burden on caregivers. A solution to this problem is to incorporate autonomous mobility functions into the wheelchair. Although many autonomous electric wheelchairs have been developed in the past, it is not reasonable to introduce them to all users of nursing homes from the standpoint of cost, charging, and maintenance. In this study, we are developing a detachable robot that can operate a manual wheelchair autonomously. The basic concept, target specifications, and design conditions are defined herein, and the results of basic experiments such as straight-line stability tests, obstacle sensor measurement tests, and self-position estimation are reported. The implementation of autonomous driving functions such as path generation and localization will be promoted in the future.

This research group has established a technology for producing a three-dimensional cell constructed using only the cell itself. This technology uses a property in which the spheroids fuse with each other. We developed a system that automates the spheroid production process to obtain reproducible spheroids and suppress variation factors that occur from human operation. However, it has become clear that the dispersion occurs in the diameter depending on the number of cells of the spheroid even if the cells are handled in the same manner. The purpose of this research is to examine an appropriate pipetting motion in accordance with the number of cells of the spheroid to be produced. Rabbit mesenchymal stem cells (rMSCs) are used as the objects. The number of cells was set to 2×10⁴, 3×10⁴, and 4×10⁴ cells/well, and the passage number as 7. The appearance of spheroids cultured using the motion programmed in accordance with each number of cells was observed every 24 hours for 5 days after seeding. The results of the analysis indicate that the optimum motion in each number of cells has been successfully specified, and reproducible spheroids have been successfully produced.

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

Many studies have been conducted on walking support for visually impaired people. However, only a few studies have contributed to the wide understanding of their surrounding environment. In this study, the focus was on the flow of people in environmental information. The flow of people is formed by the presence of many pedestrians in the surroundings of the walking environment. If visually impaired people can independently make decisions by grasping the dynamic environment of people flow, they can walk with ease and peace of mind. A method is proposed that extracts people flow information and selects the information necessary for understanding the environment. The method utilizes vibrotactile stimulation. The effectiveness of the proposed method in determining the surrounding environment and the influence of vibration information on behavior decisions were verified.

This paper proposes a real-time landing gear control system based on adaptive and high-speed 3D sensing to enable the safe landing of unmanned aerial vehicles (UAVs) on rough ground. The proposed system controls the measurement area on the ground according to the position and attitude of the UAV and enables high-speed 3D sensing of the focused areas in which the landing gears are expected to contact the ground. Furthermore, the spatio-temporal resolution of the measurement can be improved by focusing a measurement area and the proposed system can recognize the detailed shape of the ground and the dynamics. These detailed measurement results are used to control the lengths of the landing gears at high speed, and it is ensured that all the landing gears contact the ground simultaneously to reduce the instability at touchdown. In the experiment setup, the proposed system realized high-speed sensing for heights of contact points of two landing gears at a rate of 100 Hz and almost simultaneous contact on ground within 36 ms.

Cooperative swarming behavior of multiple robots is advantageous for various disaster response activities, such as search and rescue. This study proposes an idea of communication of information between swarm robots, especially for estimating the orientation and direction of each robot, to realize decentralized group behavior. Unlike the conventional camera-based systems, we developed robots equipped with a speaker array system and a microphone system to utilize the time difference of arrival (TDoA). Sound waves outputted by each robot was used to estimate the relative direction and orientation. In addition, we attempt to utilize two characteristics of sound waves in our experiments, namely, diffraction and superposition. This paper also investigates the accuracy of state estimation in cases where the robots output sounds simultaneously and are not visible to each other. Finally, we applied our method to achieve behavioral control of a swarm of five robots, and demonstrated that the leader robot and follower robots exhibit good alignment behavior. Our methodology is useful in scenarios where steps or obstacles are present, in which cases camera-based systems are rendered unusable because they require each robot to be visible to each other in order to collect or share information. Furthermore, camera-based systems require expensive devices and necessitate high-speed image processing. Moreover, our method is applicable for behavioral control of swarm robots in water.

While driving a vehicle, perceiving velocity is important for appropriate operation and is one of the most important factors for preventing collisions and traffic congestion. In contexts where perceiving velocity changes is difficult, such as on an undulating road, the velocity may exceed the speed limit or traffic congestion may occur due to heavy braking to avoid a collision. Hence, we proposed a method of modulating the perception of velocity through tactile stimulation to promote adequate operation for the driver. In contrast to methods using visual and auditory stimulation, this method has advantages of not increasing the visual cognitive load, not disturbing the enjoyment of music, and reliably stimulating the driver. In this study, we constructed a velocity perception model based on vibrotactile stimulation induced by the engine speed and proposed a method of changing the vibrotactile stimulation by altering the shift position of the transmission to modulate the perception of velocity without additional vibration actuators, regardless of the actual velocity. We measured the seat and engine vibration using two different vehicles. The results demonstrated that the peak acceleration frequencies are proportional to engine speed, indicating that the vibration depends upon the engine speed, not the velocity. We implemented a method of changing the shift position in an actual vehicle and verified the feasibility of the method through a psychophysical experiment. The results showed that drivers perceived a higher velocity with increasing engine speed and lower velocity with decreasing engine speed.

We designed a new telescopic manipulator that uses a clustered elastic convex tape. The manipulator has an ultra-wide expansion range and toughness against mechanical stress. Compared to conventional linear actuators, our convex-type manipulators have high extension range and are very lightweight. Moreover, they are compact when rolled up. The telescopic manipulators designed in the previous study had insufficient output due to structural problems and were unstable. In this study, we report a Type-K telescopic manipulator mechanism (Makijaku-Ude Type-K), which is a redesigned manipulator that can be easily used with a 300-N class power, and applied the mechanism to a three degrees-of-freedom spatial parallel-mechanism robot.

The formation control of multiple autonomous underwater vehicles (AUVs) is increasingly becoming a vital factor in enhancing the efficiency of ocean resources exploration. However, it is currently difficult to deploy such a package of AUVs for operation at sea because of their large size. The aim of our study is to create a demonstration system for formation control algorithms using actual hardware. To implement a prototype system, we developed a testbed AUV usable in a test basin and performed a simple formation control test in the Actual Sea Model Basin of the National Maritime Research Institute, Japan. Two AUVs, the simulated “virtual” leader and the developed “real” follower, communicate through an acoustic link and hence cruise to maintain a constant distance between them. Tests for more sophisticated formation control algorithms will be enabled using the system; consequently rapid implementation at sea will be realized.

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation.

Oral care is crucial to preventing diseases and maintaining the quality of life of elderly people. To create an effective training environment for nursing students, we developed a second prototype of an oral care simulator that can guide and record oral care practices. The simulator has three components: (1) a dentition model with pressure sensors, (2) a microcomputer to record signals, and (3) software for visualization. We proposed a novel mechanism to detect brushing behavior using pressure sensors and developed software to visualize the records of oral care practice. We calibrated the system to estimate the weights applied in the dentition model using a brush and verified that the calibration increased the accuracy of the estimation.