Case Study for Long- Range Beyond Visual Line of Sight Project. March 15, 2018 RMEL Transmission and Planning Conference

Transcription

1 Case Study for Long- Range Beyond Visual Line of Sight Project March 15, 2018 RMEL Transmission and Planning Conference 2014 HDR Architecture, HDR, Inc., all all rights reserved.

2 Helicopters In Transmission Inspection Helicopters will always have a place in transmission construction and repair Transmission inspection services will be predominantly replaced with Unmanned Aerial Systems (UAS) Safer Higher resolution Repeatable Autonomous Ever increasing variety of sensor technologies UAS use limited by inspection distance and cost

3 Current FAA Part 107 require an elusive waiver to fly beyond the visual line of sight (BVLOS) of the pilot Beyond Visual Line of Sight Inspections FAA is starting to support more test cases proving the safety of flying safely BVLOS There are many approaches to safely flying BVLOS (radar, ADS-B, remote ATC). We have been testing the Visual Flight Rule (VFR) approach In comparison to overall costs, the economics of helicopters to UAS starts to tip in the favor of UAS at 10 mile inspection flights BVLOS adoption will be process of sensor technology, flight technology and regulatory process

4 Craft The Four C s of BVLOS Communication Control Collision

5 Professional Grade UAVs Payload & Endurance Fill r up Plug me in

6 Communication Cellular Bonded Cellular Microwave Satellite Combination Live Video Feed Current technologies working at 5 miles

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8 Control Line of Sight (LOS) Pilot-in-command (PIC) Visual Observer (VO) PIC is always in visual sight of craft BVLOS Visual Flight Rules (VFR) Sense and Avoid Technology Real time video transmission Ability to control craft Quit on command Collision avoidance

9 Collision Avoidance

10 BVLOS Mission Planning Universal Data Collection Traditional Data Management

11 New Paradigm for Power Company Data Management

12 Supporting the Entire Organization Professional GIS Knowledge workers Executive access Public engagement Work anywhere Enterprise integration Contractors

13 Process to Successful UAS Program Development Needs Quality Sensors Identify data users Optimize data collection efforts to meet the needs of most users Precision, Accuracy, Reliability, Repeatability and Data Security Data Quality Objectives (DQOs) Select sensors to best meet DQOs Understand optimal flying conditions for sensor (altitude, spacing, speed, etc.) Craft Initial screening of UAS craft to carry sensors for expected mission length Final selection based on environmental conditions (weather, portability) Product Field check procedures for validating data collection Processing of data for specific for users (.las, DEM,.dwg) Workflow Document workflow from flight operations to data storage Encourage user feedback for continuous workflow improvement

14 UAS Sensor Technology LiDAR Terrain mapping Volume quantification Ground surface mapping Change detection Feature detection Permitting Vegetation Encroachment Multispectral / Hyperspectral VNIR and SWIR Very high resolution (0.1 m) Vegetation health Corona Coronal discharge Daylight Use Video of discharge and emitter Magnetic Subsurface Anomaly Exploration Pipeline location Well head clearance Thermal Water temperature Heat loss Mechanical stress Pipeline leaks Imagery Vegetation assessment Cultural resources Photogrammetry Infrastructure Condition Inspection

15 LiDAR versus Photogrammetry LiDAR is more accurate Penetrates vegetation Classification of features Planimetrics Not impacted by shadows Construction details PLS-CADD Ready

16 UAV Image Example from Phase I 100 Mega Pixel Camera at 20 meters Image Zoom with 1.7 mm GSD

17 LiDAR Data Collection Transmission lines Distribution lines Substations

18 UAS Craft Selection Considerations Choice of craft is based primarily on payload and endurance Use over critical infrastructure should also evaluate onboard redundancy in electronics and flight motors Imagery and LiDAR need stability Autonomous versus pilot control Mission simulation Emergency landing considerations

19 BVLOS Flight Mission 10 mile survey in central Nevada Performed with Nevada Institute for Autonomous Systems (NAIS) Flight operations performed by AviSight a DataSight partner LiDAR and imagery collect Pulse Aerospace Vapor 55 with RIEGL VUX-1 UAS LiDAR

20 UAS Altitude UAS velocity Flight pattern Data density GPS RTK Base Station Ground Panels for calibration Ground shots as quality control Flight simulation Mission planning

21 Data Collection Mission Planning for LiDAR Density Points per square meter (ppsm) Field of View Number of returns LiDAR pattern LiDAR intensity LiDAR wavelength Colorization

22 2 Return LiDAR 7 Return LiDAR

23 Colorized and non Colorized Point Cloud

24 LiDAR Scan Pattern Flight planning is different between side to side scanning versus down facing circular pattern LiDAR

25 Direct and Positional Communications During Visual LOS UAS Flight Operations

26 BVLOS for 10 Mile Transmission Line Data Collect 10 Miles

27 LiDAR Processing Inertial Measurement Unit Correction Static Base Station RTK versus PPK Core Station Calibration Quality Control Point cleanup Classification Planimetrics Digital terrain model (DTM) Digital elevation model (DEM) GNSS Antenna IMU

28 LiDAR Point Cloud Ready for Evaluation

29 Data Evaluation Data Density Drives New Methods The automatic analysis of images and videos by computers for event detection, object recognition, tracking, etc. Achieved thru writing computer programs that apply filters to remove noise and look for patterns and anomalies.

30 AI or Computer Vision is used in many places Reading license plates as you pass thru toll gates Reading money inserted into vending machines Inspecting apples to determine if there are bruises Warning drivers when they are drifting out of their lane Facial recognition/biometric security checks and many more

31 Simple Example Woodpecker Holes

32 Step 1. Train the model Include Exclude

33 Step 2: Run AI Pattern Recognition Program AI Program analyzes image: highlighting holes that are over 1 wide (yellow) highlighting holes that are over 2 wide (red)

34 Step 3: Review Results

35 Woodpecker Inspection Part 2 Inputs: Pole today and from an image 12 months ago Step 1. Train the model (model is already trained) Step 2. AI Program analyzes image A) highlighting holes that are 10% larger B) highlighting new holes Step 3: Review results Insight into Action only active poles need treatment

36 Step 3: Review Results Insight into Action only active poles need treatment

37 Inspection Workflow

38 Conclusions UAS offers a technology to safely collect higher quality data BVLOS technology is advancing and supporting regulatory adoption Understanding data needs and quality is part of UAS mission planning Data management has to be part of the planning process Larger data sets will likely require some degree of automation Long-term goal is better use of data to optimize uptime and increase worker safety

39 Questions