Preventing cross-infection is crucial for robots designed to assist medical staff in isolation wards during outbreaks of infectious diseases like COVID-19. This paper proposes a modular robotic system with a working platform and a mobile base to prevent cross-infection during item delivery and waste transport. An alignment structure for combining the two platforms is introduced, and a marker map and barcode-based destination input system were developed to allow medical staff without specialized robotics knowledge to use the system without additional training. The effectiveness of this robot's service was evaluated through a System Usability Scale (SUS) test with twenty medical staff working in isolation wards, achieving an average score of 77.12. This indicates a high level of usability, suggesting that this robot can significantly contribute to safe and efficient hospital operations during pandemic situations.

Isolation wards operate in quarantine rooms to prevent cross-contamination caused by infectious diseases. Behind the benefits, medical personnel can have the infection risk from patients and the heavy workload due to the isolation. This work proposes a robot-assisted system to alleviate these problems in isolation wards. We conducted a survey about the medical staff's difficulties and envisioning robots. Using the investigation result, we devised three valuable services using two kinds of heterogeneous robots: telemedicine, emergency alert, and delivery services by care robots and delivery robots. Our system also provides user-interactive components such as a dashboard for medical staff and a patient app for inpatients. To manage the services efficiently, we suggest the robotic system based on a central control server and a hierarchical management architecture. Through a user study, we reviewed the usability of the developed system and its future directions.

Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user's request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.

This paper aims to develop a controller for a robot manipulator equipped with series elastic actuators (SEAs) to precisely track the desired link position coping with deflections from the intrinsic compliance. The well-known time-delay estimation (TDE) technique is modified for devising the new controller. In this paper, we first report that the conventional use of a constant gain matrix for the TDE framework is insufficient for high accuracy tracking because the tracking accuracy is significantly deteriorated and the closed-loop stability may be threatened. Accordingly, the new controller employs link inertia information with dynamic coupling terms to define the gain for TDE and then employs terminal sliding mode (TSM) control to enhance robustness and convergence speed. Particularly, the modified TDE is applied in the two-staged manner which enables to compensate the complicated nonlinear dynamics terms in SEA dynamics. The TSM synergistically amalgamates the accuracy, robustness, and convergence in tracking. The proposed controller is numerically validated by comparative experiments with a SEA-driven manipulator.

This paper presents a mobile gait training system with body weight support (BWS), which relieves the patient of body weight of a prescribed amount by supporting the subject with a harness. The system is based on electrical motors to achieve system mobility according to the subject's motion of walking while it maintains a constant force on the body based on pneumatic actuators. The mobility of the system seems to be important for a correct gait pattern since the subject is able to walk on the ground actually. The system has' a simple architecture to construct, and is practical to the gait rehabilitation.

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed on the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake would not operate well because the moving object may have large differences in its position or shape, between successive images. Snake's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model (ACSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

A power assisted gait rehabilitation robot system by which a patient who has difficulty in walking can exercise is proposed. Our developed system consists of a robotic manipulator which can adjust the height of the system according to the user and a mobile base with two-driving wheels. It is similar to the architecture of a mobile manipulator. The neural network based control algorithm for a mobile manipulator used to compensate for dynamic interactions, unmodeled dynamics, and disturbances by the user on the system, is derived and experimental results are shown.