This paper presents the design and force-tracking impedance control (FTIC) of a 2 degree-of-freedom (DOF) wall-cleaning manipulator. A novel 2-DOF wall-cleaning manipulator is designed not only to ensure good contact with different shapes of walls but also to avoid various obstacles on walls efficiently. By adopting two ball-screws, 2-DOF motions of the manipulator like translation by a maximum of 150 mm and tilting by 13 degrees are realized successfully. Then, the position-based FTIC is derived to enable the proposed manipulator to not only interact with walls in a desired impedance behavior but also maintain a desired contact force. The FTIC consists of a position-based impedance control (PIC) and a disturbance observer (DOB). While the PIC ensures the excellent force tracking capability of the proposed manipulator in the steady state, the DOB is combined with the PIC to enhance its transient response, i.e., to effectively reject the disturbances caused by various shapes of walls. The extensive experiments using the test bench successfully demonstrate that the proposed manipulator can maintain a desired contact force within 4.5 N with the help of the proposed FTIC against different types of roller brushes as well as varying shapes of walls.

This article presents the design and position-tracking control of a novel two-degree-of-freedom dual-rope winch robot. It is designed to be facilely installed at both corners of a building using two synthetic ropes and to move freely on the wall plane using the ropes to perform building maintenance work. This robot consists of a dual-rope winch system responsible for its movement on a plane and a thrust module that allows the robot to remain close to the wall through the force of the propeller. In the dual-rope winch system, ascending modules driven by two pulleys and connected by differential gears are arranged symmetrically; the position and posture on the plane can be determined by controlling the ropes. The robot winds a synthetic rope around the pulley to support the load with frictional force. Precise position control is difficult to implement because of the elasticity of the rope and the slip around the pulley. Therefore, in this article, a feedforward control to predict and compensate for the slip length of the rope and a proportional-integral controller to regulate the position error owing to the dynamic characteristics of the rope are proposed. Experiments to verify the performance of the controller for various trajectories were performed on the test bench. It was confirmed that the actual trajectory deviation is within 0.5% of the target trajectory.

In this article, a grinding robot for large area and an impedance-based force control method applied to the robot are described. With the development of science and technology, the demand for various industrial robots increased, and among them, the demand and importance of grinding robots that require high risk and precision increased. In particular, research on grinding robots targeting large areas has not been conducted relatively, and accordingly, the design and control mechanism of robots specialized in large areas was needed. A robot consisting of a manipulator and a grinding module with a 2-DOF parallel structure is proposed as the design of a new grinding robot. The control method is based on impedance force control mainly used in existing grinding robots, but to overcome the limitations of using only impedance control, the impedance control via model-based prediction optimization (MPO) is proposed as a control technique for grinding robots. Experiments were conducted to verify the force tracking ability of the proposed control, resulting in a 28.1% improvement in settling time for the desired force. Even for disturbance, more improved recovery performance than conventional controllers has been verified. As a result, proposed impedance force control via MPO shows improved force tracking performance over conventional impedance control, and is presented as one of the appropriate control methods for grinding robots targeting large areas.

Façade cleaning of high-rise buildings requires automation due to the risk and inefficiency of a fall accident for human workers. Recently, due to the need for automation, various types of façade cleaning robots have been developed. In our previous study, the gondola-mounted type cleaning robot was developed, and it showed several issues. In this article, we propose an improved design feature to solve the issues. First, we modularized the robot design for lightweight and maintainability. Second, we implemented passive obstacle overcoming mechanism with tri-star wheel and compliant manipulator for minimizing uncleaned area near obstacles. Third, we developed the position sensing device for measurement and compensation of lateral disturbance occurred by swinging gondola. To verify the cleaning performance of the robot, experiments were conducted with different scenarios of cleaning general plane surface, obstacle overcoming, and lateral disturbance applied and compensated. Experimental results of both lab test and field-test performed on a real-world building were analyzed and summarized in this article.

The number of high-rise buildings is increasing worldwide, and the demand for window maintenance and cleaning has increased accordingly. However, there is an increased desire to substitute robots with human workers because of the occurrence of many accidents. Although many window cleaning robots have been developed, almost all the robots cannot passively pass obstacles on the outer walls of buildings. In this study, a window cleaning robot is provided with compliant adaptation capabilities by adding series elastic actuators (SEAs) and tri-star wheels to an older window cleaning robot. This robot has a two-degree-of-freedom manipulator, and the robot can passively overcome obstacles without any need for control. An optimization is performed for the manipulator, and experiments are performed with the manipulator to compare the respective abilities to overcome obstacles using parameters before and after the optimization. In addition, a cleaning module is constructed by scaling down the previous model. The performance of the downscaled cleaning module is validated using a window cleaning experiment.