In recent years, the deterioration of infrastructure facilities such as bridges has become a problem. Precautionary measures such as visual inspections and repair by humans are in place as countermeasures for aging, but there are problems with cost and safety in such inspections. If inspection by robots becomes possible, both will be improved, which will greatly contribute to the maintenance of infrastructure facilities. In this paper, we propose a localizational position specifying system for a robot supporting bridge inspection of the moving robot under the bridge to support inspection of the bridge. Since the robot must travel on the steel structures at the bottom of the bridge, strong permanent magnets need to be installed on the edge parts in the wheels. Also, it is not possible to receive the coordinate information from the satellite as it travels at the bottom of the bridge. Therefore, a 1-dimensional low-cost LiDAR sensor and a stepping motor are used to implement as a planar LiDAR sensor which rotates 360 degrees, specify the position of the current robot by obtaining distance and direction data from fixed feature points. The localizational position specification through the planar LiDAR are examined by the experiment. As a result, it was possible to estimate the localizational position of the robot.

No abstract

In recent years, the deterioration of infrastructure facilities such as bridges has become a problem. Precautionary measures such as visual inspection and repair by humans are in place as countermeasures for aging; however, there are issues with cost and safety in such inspections. If inspection by robots becomes possible, both these aspects will be improved, which will significantly contribute to the maintenance of infrastructure facilities. In this paper, we propose a complex image processing technique to specify the location of feature points as coordinates through smartphone cameras to obtain the location information of feature points needed for positioning BIREM-IV-P developed to support bridge inspection. The corners located in the bridge inspection environment are used as feature points, and the corners are specified using Harris corner detection, which is a conventional corner detection method, to obtain the position of the feature points. In addition, to compensate for the shortcomings of Harris corner detection, a line segment in the image is detected using the Hough transform, and the intersection points of the line segments are recognized as corners. By combining the results of the two detection methods in this manner, the target feature points can be accurately specified. Then, the position of the feature points of the specified image coordinate system can be changed to the world coordinate system. As a result, it was possible to detect the location of the target feature points in a three-dimensional coordinate system.

The appropriate selection of arrows in the sport of archery is important to the achievement of high-quality results. In this regard, a shooting machine that is compatible with the wide variety of available bows and arrows is necessary. However, bow strength and arrow length vary among different athletes. It is also important to develop a shooting machine that reproduces the movement of the right fingers that releases an arrow, and the forward jump of a bow after shooting an arrow. In this study, a shooting machine was developed that considered these factors. Its efficacy was examined in terms of its ability to determine the characteristics of each arrow, and to distinguish between normal and flawed arrows. Based on the experimental results, we identified the factors that affected the shooting accuracy of arrows. In addition, the developed shooting machine was able to distinguish between normal and flawed arrows.