Jiang Yong

Research of power transmission line maintenance robots in SIACAS

To monitor the running conditions and find out the damages of the power grids, the robotic technologies have been applied to conduct the inspection tasks of power transmission lines (PTLs) instead of worker or helicopter. Supported by the national high technology research and development program of China from 2002, the state key laboratory of robotics, Shenyang institute of automation, Chinese academy of sciences (SIACAS), has been focusing on the development of the inspection robot for 500kV extra high voltage (EHV) PTLs. Up to now, three generations of the inspection robot prototypes have been developed. In this paper, an overview of the research for these prototypes by SIACAS is given. Then, two latest developments which include a PTLs inspection robot and an insulator detection robot are introduced, respectively. The key technologies such as mechanism, control system, and operation modes, are described in detail. Field experimental results show that the robot prototypes can carry out the practical inspection tasks for 500kV EHV PTLs.

A Novel Approach to Fault Detection and Identification in Suction Foot Control of a Climbing Robot

This paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robots states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approachThis paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robots states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approach.