Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation
Abstract
:1. Introduction
2. Research Background
2.1. Examples of Drone Missions in Confined Spaces
2.2. Challenges of Flying Drones in Confined Spaces
2.3. Commercial off-the-Shelf Indoor Inspection and Map** Drones
2.4. Outdoor Aerial Stockpile Volume Estimation
2.5. Dust Effects on LiDAR Sensors
3. Current Health and Safety Challenges in Cement Plants
3.1. Overview
3.2. Revisiting Confined Space Safety Challenges
Country/ Region | Period | Incidents | Fatalities | Fatality Rate per 100,000 Workers | Source |
---|---|---|---|---|---|
Australia | 2000–2012 | 45 | 59 | 0.05 | [65] |
New Zealand | 2007–2012 | 4 | 6 | 0.05 | [54] |
Singapore | 2007–2014 | N/A | 18 | 0.08 | [54] |
Quebec, Canada | 1998–2011 | 31 | 41 | 0.07 | [50] |
British Columbia, Canada | 2001–2010 | 8 | 17 | N/A | [54] |
USA | 1980–1989 | 585 | 670 | 0.08 | [66] |
USA | 1997–2001 | 458 | 0.07 | [67] | |
USA | 1992–2005 | 431 | 530 | 0.03 | [68] |
UK and Ireland | N/A | N/A | 15–25/year | 0.05 | [69,70] |
Italy | 2001–2015 | 20 | 51 | N/A | [71] |
Jamaica | 2005–2017 | 11 | 17 | N/A | [71] |
3.3. Indoor Stockpile Volume Estimation
3.4. Drones Safety Regulations
4. Proposed Drone-Assisted Solution for Stockpile Volume Estimation
4.1. Overview
4.2. Simulation Framework
4.2.1. Simulation Setup and Selection of Sensors
4.2.2. Indoor Localisation System
4.2.3. Locating and Filtering Point Clouds
4.2.4. Surface Generation
4.2.5. Mission Design
4.2.6. Simulation Results and Discussions
4.2.7. Stockpile Volume Estimation Using 3D Static Scanners
4.3. Industrial Case Study
4.3.1. Instrumentation
4.3.2. Data Collection and Processing
4.3.3. Results from Flight Tests
5. Cost-Benefit Analysis
6. Concluding Remarks and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study | Mission | Drone Configuration and Diagonal Size | Sensor Employed | Localisation Approach |
---|---|---|---|---|
[12] | Building a 3D map of gas distribution | Crazyflie 2.0 (92 mm) | Metal oxide (MOX) gas sensor | External UWB radio transmitters |
[13] | Indoor map** and localisation | Phantom 3 Advanced Quadcopter (350 mm) | Two 2D LiDARs and an IMU | Point-to-point scan matching algorithm and Kalman filter |
[14] | Searching for survivors in collapsed buildings or underground areas | DJI Matrice 100 (650 mm) | 2D LiDAR and infrared depth camera | RVIZ package within ROS |
[15] | Inspecting sewer systems | DJI F450 (450 mm) | Four 1D LiDARs and a camera | Two PID controls |
[16] | Odour-finding and localisation | Crazyflie 2.0 (92 mm) | Electroantennogram (EAG), camera, and IR | 2D cast-and-surge algorithm |
[17] | Detecting fire and smoke | Crazyflie 2.0 (92 mm) | IMU and camera | SLAM and pose-graph optimization algorithms |
[18] | Radiation source localisation and map** | Three DJI F450 (450 mm) | Kromek cadmium zinc telluride (CZT) detector | Contour map** algorithm and source seeking |
[19] | Map** and inspection of underground mines | DJI Wind 2 (805 mm) | RGB, multispectral and thermal cameras, and 3D LiDAR | Emesent Hovermap device |
[20] | Map** underground mines | DJI Matrice 100 (650 mm) | 3D LiDAR, a mono camera, and a high-performance IMU | LiDAR Odometry And Map** and Robust Visual Inertial Odometry |
Stereo camera, thermal vision camera, and a high-performance IMU | Robust Visual Inertial Odometry | |||
[21] | Fully autonomous flight in underground mines | DJI M210 (643 mm) | Ultrasonic range sensor and stereo camera | Visual Odometry |
LiDAR | Benewake TFmini | Hokuyo UTM-30LX | Livox Mid-40 |
---|---|---|---|
(1D) | (2D) | (3D) | |
FoV | |||
Range (m) | 0.3–12 | 0.1–30 | 260 |
Resolution (point) | 1 | 1080 | ≈3200 |
Point Rate (points/s) | 100 | 43,200 | 100,000 |
Power Consumption (W) | 0.12 | 8 | 10 |
Weight (gm) | 10 | 370 | 710 |
Price (£) | 33 | 4169 | 539 |
Missions | Details | Benewake TFmini(1D LiDAR) (FoV: 2.3) | Hokuyo UTM-30LX (2D LiDAR) (FoV: 270) | Livox Mid-40 (3D LiDAR) (FoV: 38.4) |
---|---|---|---|---|
Open area | Generated surface | Figure 10b | Figure 10d | Figure 10f |
Flight time (min) | 8.36 | 8.36 | 8.36 | |
Number of collected point clouds | 15,366 | 16,595,280 | 49,171,200 | |
Estimated volume (m) | 3224.6 | 3131.3 | 3076.8 | |
Error (%) | +3.06 | +0.07 | −1.67 | |
Fully confined storage | Generated surface | Figure 11b | Figure 11d | Figure 11f |
Flight time (min) | 5.55 | 5.55 | 5.55 | |
Number of collected point clouds | 10,260 | 11,080,800 | 32,832,000 | |
Estimated volume (m) | 2838.3 | 3101.3 | 3130.7 | |
Error (%) | −9.36% | −0.83% | +1.66% |
Details | Benewake TFmini | Hokuyo UTM-30LX | Livox Mid-40 |
---|---|---|---|
(1D LiDAR) | (2D LiDAR) | (3D LiDAR) | |
Generated surface | Figure 15b | Figure 15d | Figure 15f |
Flight time (min) | 5.55 | 5.55 | 5.55 |
Number of collected point clouds | 9952 | 10,855,080 | 32,163,200 |
Estimated volume (m) | 2876.3 | 3812.6 | 3486.4 |
Error (%) | −25.8 | −2.41 | −9.84 |
Noise | 1D LiDAR | 2D LiDAR | 3D LiDAR | |
---|---|---|---|---|
Outdoor | Excluded | +3.06% | +0.07% | −1.67% |
Figure 8a | Included | +3.06% | +0.22% | −1.36% |
Indoor 1st stockpile | Excluded | −9.36% | −0.83% | +1.66% |
Figure 8b | Included | −9.25% | +0.59% | +2.15% |
Indoor 2rd stockpile | Excluded | −25.8% | −2.41% | −9.84% |
Figure 14 | Included | −25.3% | −1.94% | −9.06% |
Drone-Assisted Map** | 3D Static | |||
---|---|---|---|---|
1D LiDAR | 2D LiDAR | 3D LiDAR | Scanners | |
Indoor 1st stockpile (Figure 8b) | −9.36% | −0.83% | +1.66% | +0.21% |
Indoor 2rd stockpile (Figure 14) | −25.8% | −2.41% | −9.84% | −7.6% (+0.59% *) |
Cost Element | Sub-Element | Quantity | Approximate Cost (£) | |
---|---|---|---|---|
Unit | Total | |||
Man-hours | Planning | 1 | 8 h @ £25 per hour | 200 |
Mission | 2 | 2 × 8 h @ £25 per hour | 400 | |
Data analysis | 1 | 8 h @ £25 per hour | 200 | |
Personal protective equipment (PPE) | Dust masks (99.99% filtration accuracy) | 2 | 20 | 40 |
Safety goggles | 2 | 9 | 18 | |
Safety boots | 2 | 30 | 60 | |
High visibility overalls | 2 | 40 | 80 | |
Safety gloves | 2 | 3 | 6 | |
Ear protectors | 2 | 3 | 6 | |
Hard hats (helmets) | 2 | 6 | 12 | |
Transportation | Train | 2 | 15 | 30 |
Taxi | 2 | 35 | 70 | |
Instrumentation | Drone (1D LiDAR approach) | 1 | 1000 | 1000 |
Spares and tools | 1 set | 150 | 150 | |
Laptops | 2 | 750 | 1500 | |
Annual drone insurance [97] | 1 | 180 | 180 | |
Annual CAA fees | 1 | 750 | 750 | |
Miscellaneous | Refreshment and stationery | 2 | 25 | 50 |
Total (£) | 4752 |
Study | Mission | Task Frequency | Average Size of Manpower at Risk | Environmental Complexity | Accuracy Level | Impact of Task on Operation | Approximate Cost | Cost-Benefit Priority Factor |
---|---|---|---|---|---|---|---|---|
[12] | Building a 3D map of gas distribution | 1 | 5 | 1–2 | 2 | 3–4 | 5 | 5–13.3 |
[13] | Indoor map** and localisation solution | 1 | 1 | 1–3 | 5 | 1 | 2–3 | 0.33–1.5 |
[14] | Searching for survivors in collapsed buildings or underground areas | 2 | 5 | 5 | - | 4 | 4 | - |
[15] | Inspecting sewer systems | 2 | 2–3 | 4–5 | 4 | 2 | 5 | 21–40 |
[16] | Odour-finding and localisation | 2 | 1–2 | 3–5 | 2 | 3 | 5 | 6–20 |
[17] | Detecting fire and smoke | 3 | 1–2 | 3–5 | 5 | 5 | 5 | 37.5–125 |
[18] | Localisation and map** a radiation source | 1 | 5 | 5 | 4 | 5 | 2–3 | 33.3–50 |
[19] | Map** and inspection of underground mines | 3 | 5 | 5 | 5 | 4 | 1 | 50 |
[20] | Map** underground mines | 3 | 5 | 5 | 5 | 4 | 2 | 100 |
[21] | Fully autonomous flight in underground mines | 1 | 5 | 5 | 5 | 4 | 2–3 | 33.3–50 |
Current study | Confined space inspection and stockpile estimation | 4 | 5 | 5 | 5 | 4 | 5 | 333 |
Ranking | Task Frequency | Average Size of Manpower at Risk | Environmental Complexity | Accuracy Level | Impact of Task on Operation | Approximate Cost |
---|---|---|---|---|---|---|
5 | Very high (hourly-daily) | Very high (>5 employees) | Extremely harsh (e.g., extremely high hazard due to dust-laden air, high temperatures, high humidity, poor visibility, poor communication signals, confined space, uneven surfaces, etc.) | Very high (0–5% error levels) | Major (operation stops) | ≤£1000 |
4 | High (weekly-monthly) | High (3–5 employees) | Harsh (e.g., significant hazard levels) | High (>5–10% error levels) | Significant (significant impacts on quality, stock balance, working capital, safety, etc.) | £1000–£3000 |
3 | Moderately (3–6 monthly) | Moderate (2–3 employees) | Moderately (moderate hazard levels) | Moderate (>10–15% error levels) | Important (important but less significant impacts on quality, stock balance, working capital, safety, etc.) | >£3000–£5000 |
2 | Rarely (yearly) | Low (1 employee) | Friendly (friendly work environment with insignificant hazard levels) | Low (>15–20% error levels) | Minor (minor impacts on quality, stock balance, working capital, safety, etc.) | >£5000–£10,000 |
1 | Very rarely (>yearly) | Very low (completely autonomous) | Extremely friendly (very friendly work environment with very insignificant hazard levels) | very low (>20% error levels) | Very minor (very minor impacts on quality, stock balance, working capital, safety, etc.) | >£10,000 |
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Alsayed, A.; Yunusa-Kaltungo, A.; Quinn, M.K.; Arvin, F.; Nabawy, M.R.A. Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sens. 2021, 13, 3356. https://doi.org/10.3390/rs13173356
Alsayed A, Yunusa-Kaltungo A, Quinn MK, Arvin F, Nabawy MRA. Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sensing. 2021; 13(17):3356. https://doi.org/10.3390/rs13173356
Chicago/Turabian StyleAlsayed, Ahmad, Akilu Yunusa-Kaltungo, Mark K. Quinn, Farshad Arvin, and Mostafa R. A. Nabawy. 2021. "Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation" Remote Sensing 13, no. 17: 3356. https://doi.org/10.3390/rs13173356