An Intelligent Inspection Robot for Underground Cable Trenches Based on Adaptive 2D-SLAM

Abstract

With the rapid growth of underground cable trenches, the corresponding inspections become a heavy burden, and an intelligent inspection robot for automatic examinations in underground cable trenches would be a suitable solution. To achieve this, this paper establishes one new navigation methodology for intelligent inspection robots, especially when applied in complex scenarios and the corresponding hardware. Firstly, to map the underground trenches with higher precision, an improved graph optimization cartographer-SLAM algorithm is proposed, which is based on the combination of depth camera and LIDAR. The depth image is converted into pseudo laser data, and fused with LIDAR data for calibration. Secondly, to overcome the low precision of the Laser odometer due to the uneven ground, an adaptive keyframe selection method is designed. Thirdly, the forward A* model is presented, which has been adjusted in three aspects, including the convergence of node searching, the cost function, and the path smoothness, to adapt to the narrow underground environment for global path planning. Fourthly, to realize dynamic obstacle avoidance, an improved fusion scheme is built to integrate the proposed global path planning algorithm and the dynamic window approach (DWA). In the case study, the simulation experiments showed the advantage of the forward A* algorithm over the state-of-the-art algorithm in both time consumption and the number of inflection points generated, the field tests illustrated the effect of the fusion of depth camera images and LIDAR. Hence, the feasibility of this navigation methodology can be verified, and the average length of path and time consumption decreased by 6.5% and 17.8%, respectively, compared with the traditional methods.

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

“…Mobile robots utilize LiDAR technology for external environment perception [31]. LiDAR enables pose estimation and the construction of 2D maps for mobile robots [32] through the processing of scanned data [33]. In this technology, the forward direction of the 2D LiDAR is defined as the polar axis, while the rotation center of the LiDAR ranging core is set as the pole, establishing a polar coordinate system.…”

Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening

“…In particular, infrared (IR) cameras have received considerable attention for health monitoring in the application field of electric and mechanical facilities because they provide meaningful information on the thermal energy emitted from an object of interest when a fault occurs. Intensive studies have been conducted to detect anomalies using IR cameras [15][16][17][18][19]. Fault detection using an aerial system deploying an IR camera was proposed for anomaly detection in photovoltaic farms [15,16] and overhead transmission lines [17].…”

“…Fault detection using an aerial system deploying an IR camera was proposed for anomaly detection in photovoltaic farms [15,16] and overhead transmission lines [17]. A patrol inspection robot was proposed by deploying an IR camera to monitor the temperature of underground power facilities (UPF) with 2-D simultaneous localization and mapping [18]. Object detection through a customized neural network has also been proposed to identify defects in a thermal image [19].…”