A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data
Abstract
:1. Introduction
- Integrate advanced LiDAR technology and CIR technology to get unique CIR LiDAR data. CIR LiDAR, along a ground wire, would not be subject to topographic restriction like airborne LiDAR; CIR LiDAR can also scan objects at a closer range to obtain more fine-scale LiDAR data like vehicle-borne LiDAR. CIR LiDAR can also provide high-precision POS data that could represent the orientation and shape of power lines in a span segment.
- Propose an autonomous inspection method based on CIR LiDAR data, combine the spatial information superiority of LiDAR and texture information advantage of image, and solve the practical problems in the autonomous inspection of transmission line at present.
- Automatically identify inspection targets using point cloud and image processing technology in preliminary inspection, operate CIR along a ground wire to the specified positions based on 3D precise information of inspection targets, transfer angles or directions of the PZT camera to take high-quality images, and save the inspection information of targets into the inspection database. This will greatly improve the efficiency and accuracy of inspection, providing a theoretical foundation for intelligent inspection.
2. Hardware
2.1. System Structure
2.2. System Integration
3. Methodology
3.1. Preliminary Inspection
3.1.1. Point Cloud Segmentation
- (1)
- Point cloud partition of a span segment based on POS data. In the process of inspection, the motion of CIR presents uphill–downhill–uphill. According to work characteristics and body angle data measured by obliquity sensors, suspension points of ground wire are assigned. In this way, multi-segment transmission lines can be partitioned into single segment transmission lines in point cloud data.
- (2)
- Ground point filtering by the elevation threshold value of POS. In addition to transmission line points of a span segment, there are also a few ground points and remaining surface points. The transmission line points in a span segment are generally higher than the ground points, so the POS elevation threshold is defined to quickly remove all ground points and most of the remaining surface points.
- (3)
- POS-based structured partition. The filtered transmission line point cloud and POS data are projected to the optimal coordinate plane (OCP) by the coordinate transformation. Then, we establish the extraction model by fitting POS data. The ground wire is subject to additional sag due to the additional weight of CIR, as illustrated in Figure 4. The fitting POS-based model is revised with the additional sag function, [32]. Through the POS extraction model, transmission line point clouds are divided into single lines in a span segment, at the same time retaining waiting inspection target point clouds around the line.
3.1.2. Target Identification and Positioning
3.1.3. CIR Motion Trajectory Modeling
- How many waiting-inspection targets (refers to these inspection targets that need to be inspected again in the autonomous inspection) on transmission lines are there?
- Each waiting-inspection target belongs to one transmission line;
- The identification result and space position of each inspection target;
- The trajectory model of the ground wire.
3.2. Autonomous Inspection
3.2.1. Coordinate System Modeling
3.2.2. Autonomous Inspection Planning
3.2.3. Autonomous Inspection Procedure
4. Results
4.1. Test Site Experiment
4.2. Actual Line Experiment
5. Discussion
6. Conclusions
- (1)
- CIR can become a new type of carrying platform to collect LiDAR data. Because CIR always moves along the ground wire, the POS data can be used to deal with a transmission line point cloud and build up a CIR motion trajectory model. In addition, CIR can closely scan transmission line corridors, so the point cloud is very dense. The abnormal points can be clearly presented in the transmission line point cloud.
- (2)
- The proposed method mainly includes two inspection steps. The preliminary inspection can find the abnormal points through point cloud distribution, and use a hierarchical classification strategy to determine their classes and 3D positions. The autonomous inspection can generate the inspection sequences according to the inspection target information obtained in preliminary inspection, operate CIR to reach the specified points in accordance with inspection planning, and take images of the inspection targets with the PTZ cameras. Therefore, in the post-processing phase, it is no longer necessary to extract useful information from the many inspection images or videos, but only to make a final judgement by contrasting images at specified points and preliminary inspection results. This method can effectively improve the intelligence level of the current transmission line inspection, greatly reducing manpower and improving inspection accuracy.
- (3)
- In test site experiments, we designed eight inspection targets including three classes of transmission line inspection. Experimental results show that CIR can move to the specified set points in turn and take clear images to assist the artificial judgement for the wrong classifications or hard identifications, which verifies the effectiveness of the proposed method. Actual line experiments prove that the autonomous inspection system can collect the required data. CIR can inspect the three dampers in the span segment and transfer the PTZ cameras to take images, which verifies the feasibility of the proposed method.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Component Name | Mode | Amount | ||
---|---|---|---|---|
CIR | CIR body | Walking through mode | 1 | |
PTZ camera | SNC-WR630 | 2 | ||
LiDAR | Laser scanner | VLP-16 | 1 | |
POS system | APX-15UAV | 1 | ||
Mobile-end antenna | HX-CA7606A | 2 | ||
Direction-finding receiver | FLEX6-D2S-Z00-00N | 1 | ||
Memory card | 128 G | 1 | ||
CIR base station | CIR base station | Customization | 1 | |
GNSS base station | Reference station receiver | SDI-228 | 1 | |
Reference station antenna | HY-BGLRC08R | 1 |
Class Name | Inspection Target | Feature Description | Density | Contour Feature | |
---|---|---|---|---|---|
Fitting (F) | Damper | F1 | Artificial facility Attached to the power line | Dense | “T” shape |
Spacer | F2 | Artificial facility Attached to the power line | Dense | “X” shape | |
Line (L) | Broken strand | L1 | Attached to the power line | Dense | Curve shape |
Environment (E) | Tree crown | E1 | Under the power line Large size | Dense | Sphere shape |
Overrun building | E2 | Under the power line Large size | Dense | Planar shape | |
Attachment | E3 | Attached to the power line Small size | Dense | Irregular shape |
Number | Line Number | Inspection Point | Coordinate in ECS | Class | Inspection Sequence |
---|---|---|---|---|---|
① | A-phase | A | AG | F1 | LP1-LNA-GF1-ID1 |
② | Ground wire | B | BG | E3 | LP1-LNG-GE3-ID2 |
③ | C-phase | C | CG | E1 | LP1-LNC-GE1-ID3 |
④ | B-phase | D | DG | L1 | LP1-LNB-GL1-ID4 |
⑤ | A-phase | E | EG | F1 | LP1-LNA-GF1-ID5 |
Target | Relative Coordinate (m) | Preliminary Result | Inspection Sequence | PTZ Camera Pan/tilt | Image | Judgement | |||
---|---|---|---|---|---|---|---|---|---|
⑧ | 6.5 | 1.58 | −0.11 | GE2 | LP1-LNA-GE2-ID1 | −96.0 | 6.53 | | Y |
⑦ | 5.62 | 0.74 | −1.57 | GL1 | LP1-LNB-GL1-ID2 | −93.6 | 5.20 | | N |
② | 6.57 | 1.68 | −5.22 | GM1 | LP1-LNA-GM1-ID3 | −117.0 | −18.10 | | Y |
④ | 5.37 | 1.75 | −5.31 | GM1 | LP1-LNB-GM1-ID4 | −122.7 | −12.73 | | Y |
⑤ | 6.65 | 1.95 | −6.04 | GE3 | LP1-LNA-GE3-ID5 | −130.0 | −21.86 | | Y |
⑥ | 5.64 | 1.93 | −6.19 | GE3 | LP1-LNB-GE3-ID1 | −135.2 | −18.15 | | Y |
① | 6.74 | 1.84 | −7.10 | GM1 | LP1-LNA-GM1-ID2 | −147.9 | −27.28 | | Y |
③ | 0.76 | 1.89 | −7.11 | GM1 | LP1-LNB-GM1-ID3 | −152.2 | −21.06 | | Y |
Target | Relative Coordinate (m) | Preliminary Result | Inspection Sequence | PTZ Camera Pan/tilt | Image | Judgement | |||
---|---|---|---|---|---|---|---|---|---|
② | 2.05 | −12.4 | −0.80 | GM1 | LP1-LNB-GM1-ID1 | −26.1 | 49.3 | | Y |
① | 0.55 | −5.7 | −0.85 | GM1 | LP1-LNA-GM1-ID2 | −53.6 | 51.7 | | Y |
③ | 1.05 | −18.9 | −0.88 | GM1 | LP1-LNC-GM1-ID3 | −80.2 | 41.8 | | Y |
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Qin, X.; Wu, G.; Lei, J.; Fan, F.; Ye, X.; Mei, Q. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors 2018, 18, 596. https://doi.org/10.3390/s18020596
Qin X, Wu G, Lei J, Fan F, Ye X, Mei Q. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors. 2018; 18(2):596. https://doi.org/10.3390/s18020596
Chicago/Turabian StyleQin, Xinyan, Gongping Wu, Jin Lei, Fei Fan, Xuhui Ye, and Quanjie Mei. 2018. "A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data" Sensors 18, no. 2: 596. https://doi.org/10.3390/s18020596
APA StyleQin, X., Wu, G., Lei, J., Fan, F., Ye, X., & Mei, Q. (2018). A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors, 18(2), 596. https://doi.org/10.3390/s18020596