Among many pipes in a nuclear power plant, the spray pipes in the reactor building are one of the most important pipes in view of their function and safety aspects. However, it is very difficult to manually reach and inspect the pipes for defects or damage, because these pipes are installed in very high places. To carry out this kind of inspection more easily, we developed a mobile robot to climb up and down and to cross over such pipes. A mobile robot should be small and light enough that it can be practically and safely operated in a nuclear power plant. Our robot is able to overcome obstacles such as valves, pipe flanges, and T-shaped branches, and it also meets the requirements of fail-safe, autonomous grasping, and self-power without the cables to the remote control station. The robot has a five-degree-of-freedom manipulator and two grippers and moves along the cylindrical pipes bypassing the obstacles. The robot should be able to grasp the next pipe autonomously, because the robot works in places high off the ground where the remote operator cannot see the next pipe for the robot to grasp. This article proposes a vision-based scheme for grasping a cylindrical pipe semiautonomously and describes its solution along with the forward kinematics and inverse kinematics of the mobile robot. The configuration of the pipe-climbing robot, including its hardware and software, is described, and the robot control with visual grasping is explained. The robot can be used practically for spray-pipe inspection as well as many potential other applications, such as inspection of the roof frame of a stadium consisting of pipes.
This paper proposes a deep‐learning‐based wireless sensor system that uses an embedded two‐dimensional (2D) light‐emitting diode (LED) array to display measured sensor data and remote data transmission to detect nuclear power plant (NPP) equipment defects. The frequent use of electromagnetic waves often interferes with the operation of NPP. Therefore, we devised a wireless image transmission network using a 2D LED array panel that includes a sensor module and a camera to capture LED array images. Based on the experimental results, the proposed method adopting deep‐learning‐based LED array data extraction produces reliable digital data restoration performance in terms of classification accuracy, even in a complex noise environment.
There are four methods such as 1:1, 1:N, N:1, and N:N in the tele-operating system for control the remote side robot. The operator must know the information of surroundings of the robot, collision possibility of the mobil equipment, and force feedback of the manipulator. The time delay problem occurs in the tele-operating and it causes vibration and expressive power of the manipulator owing to bidirectional force feedback. N:N control technique having been developed lately has a switching algorithm for the operator to select the target robot or it's partial equipment. When multi-operators work together to accomplish a task with multi-robots, the switching facility must be offered. And the automatic tuning skill to generalize the operator's tendency is needed also. We describes the methodologies and skills for developing a haptic-based tele-operating environments to makes it possible to control the remote multi-robots with multi-operators in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.