Position recognition is one of the core technologies for driving a robot because of differences in environment and rapidly changing situations. This study proposes a strategy for estimating the position of a camera mounted on a mobile robot. The proposed strategy comprises three methods. The first is to directly acquire information (e.g., identification (ID), marker size and marker type) to recognize the position of the camera relative to the marker. The advantage of this marker system is that a combination of markers of different sizes or having different information may be used without having to update the internal parameters of the robot system even if the user frequently changes or adds to the marker’s identification information. In the second, two novel markers are proposed to consider the real environment in which real robots are applied: a nested marker and a hierarchical marker. These markers are proposed to improve the ability of the camera to recognize markers while the camera is moving on the mobile robot. The nested marker is effective for robots like drones, which land and take off vertically with respect to the ground. The hierarchical marker is suitable for robots that move horizontally with respect to the ground such as wheeled mobile robots. The third method is the calculation of the position of an added or moved marker based on a reference marker. This method automatically updates the positions of markers after considering the change in the driving area of the mobile robot. Finally, the proposed methods were validated through experiments.

Many stroke survivors have impaired balance control. This study assesses the effects of ankle stretching exercising with our recently developed Motorized Ankle Stretcher (MAS) technology compared to exercising with a stretching board, on stroke survivors’ balance control. Sixteen stroke survivors were randomly assigned to a control group (CG) and an intervention group (IG). The CG and IG performed ankle stretching exercises with the stretching board and MAS, respectively, two days per week for four consecutive weeks. Balance performance was assessed by a Sensory Organization Test (SOT) at the beginning of week 1 (pre-assessment), at the end of week 4 (post-assessment), and 1 month after week 4 (retention-assessment). Balance performance was quantified by a root-mean-square (RMS), range, and area of body’s center of pressure (COP) data obtained by the SOT. The IG significantly improved COP RMS and COP range in the anterior-posterior direction at the post- and retention-assessments compared to the pre-assessment. The IG also significantly improved COP area at the retention-assessment compared to the pre-assessment. The improvements were not observed in the CG. The findings of this study have clinical implications since the MAS potentially could be used in both domestic and clinical settings.

Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of human–machine interactions, it needs to be soft to ensure comfort and safety, and it should be easily customizable and inexpensive. The purpose of this study is to estimate 3D contact position of a novel image-based areal soft tactile sensor (IASTS) using printed array markers and multiple cameras. First, we introduce the hardware structure of the prototype IASTS, which consists of a soft material with printed array markers and multiple cameras with LEDs. Second, an estimation algorithm for the contact position is proposed based on the image processing of the array markers and their Gaussian fittings. A series of basic experiments was conducted and their results were analyzed to verify the effectiveness of the proposed IASTS hardware and its estimation software. To ensure the stability of the estimated contact positions a Kalman filter was developed. Finally, it was shown that the contact positions on the IASTS were estimated with a reasonable error value for soft haptic applications.