Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities
Abstract
1. Introduction
2. Literature Review
2.1. Conventional Video Collection for Construction Monitoring
2.2. Data Collection Using UAV for Construction Monitoring
Ref. | Sensor Type | Operation Environment | Waypoints Generation Method | Routing Algorithm | Schedule Considered | Application | Type of Target |
---|---|---|---|---|---|---|---|
[35] | Camera | Outdoor | Predefined | A* | No | Inspection | Constructed objects |
[36] | n/a | Indoor | Predefined | A* | No | Inspection | Constructed objects |
[37] | Camera | Outdoor/ Indoor | Sampling based on coverage | LKH and RRT* | No | 3D reconstruction and inspection | Constructed objects |
[38] | Camera | Outdoor | Sampling based on coverage, sensor spec., and overlapping rate | DPSO and A* | No | 3D reconstruction and inspection | Constructed objects |
[39] | Camera | Outdoor | Refining the nun-occluded sampled viewpoints to minimize the number of waypoints | A* | No | Inspection | Constructed objects |
[40] | Laser scanner | Outdoor | Sampling based on coverage, sensor spec., overlapping rate, and criticality levels of different zones | GA and A* | No | Inspection | Constructed objects |
[12] | Camera | Outdoor | Sampling | SVRP from ArcGIS | No | 3D reconstruction | Constructed objects |
[41] | Camera | Outdoor | OABC Algorithm | No | 3D reconstruction | Constructed objects | |
[11] | Camera | Outdoor/ Indoor | HEDAC | No | 3D reconstruction | Constructed objects | |
[10] | Camera | Outdoor | Sampling based on 3D grid-based flight plan template and VL-MOGA | Yes | 3D reconstruction, progress monitoring | Constructed objects | |
[42] | Camera | Outdoor | Sampling waypoints in the areas of interest | Improved ACO algorithm | No | Construction safety inspection | Safety risks on construction site |
This paper | Camera | Outdoor | NSGA-II | A* and random-key GA | Yes | Activity monitoring | Construction activities |
2.3. Challenges in Applying Activity Recognition Techniques on Aerial Videos
3. Proposed Method
3.1. Method Overview
3.2. Simulation-Environment-Preparation Module
3.3. VPs-Optimization Module
3.4. Path-Optimization Module
4. Implementation and Case Study
4.1. Implementation
4.2. Case Study
4.3. Pilot Test for Evaluating the VPs-Optimization Module
4.4. Results of the Case Study
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Camera Type | Environment | Optimization Method | Simulation Platform | Schedule Considered? | Application |
---|---|---|---|---|---|---|
[30] | Fixed camera | Indoor | PICEA | 3D | No | General site monitoring |
[32] | Fixed camera | Outdoor | NSGA-II | 2D | No | General site monitoring |
[31] | Fixed camera | Outdoor | Semantic-Cost GA | 2D | No | General site monitoring |
[33] | PTZ camera | Outdoor | Modified GA | 3D | No | Safety monitoring |
[5] | Fixed camera | Indoor | PMGA | 2D | Yes | Activity monitoring |
[6] | Fixed camera | Outdoor | NSGA-II | 3D | Yes | Safety monitoring |
Workspace | Number of VPs | Range of Gene Value | Example Gene | Selected VP | Random Key | Visiting Order |
---|---|---|---|---|---|---|
A | 4 | [0, 4) | 3.83 | 0.83 | 3 | |
B | 5 | [0, 5) | 3.25 | 0.25 | 1 | |
C | 3 | [0, 3) | 1.77 | 0.77 | 2 |
ID | Center Point Coordinates and Height of Workspace | Range of Attribute Values of Each Search Space | ||||||
---|---|---|---|---|---|---|---|---|
(m) | (m) | h (m) | (m) | (m) | (m) | φ’ (°) | θ (°) | |
WS1 | 67.0 | 26.0 | 5.0 | [38.0, 96.0] | [−3.0, 55.0] | [10.3, 28.5] | [−45, 45] | [15, 60] |
WS2 | 1.0 | 66.0 | 12.0 | [−26.9, 28.9] | [38.1, 93.9] | [17.0, 31.0] | [−45, 45] | [15, 60] |
WS3 | 1.0 | 40.0 | 12.0 | [−26.9, 28.9] | [12.1, 67.9] | [17.0, 31.0] | [−45, 45] | [15, 60] |
WS4 | 57.0 | −29.0 | 8.0 | [28.4, 85.6] | [−57.6, −0.4] | [13.0, 30.0] | [−45, 45] | [15, 60] |
VP ID | X (m) | Y (m) | Z (m) | (°) | (°) | Coverage (%) | Distance (m) | Fitness Value (%) | |
---|---|---|---|---|---|---|---|---|---|
Scenario 1 | 1 | 14 | 59 | 31 | 54 | −12 | 96.64 | 28.81 | 78.69 |
2 | 14 | 59 | 30 | 56 | −4 | 96.61 | 27.95 | 79.66 | |
3 | 14 | 59 | 29 | 56 | −22 | 96.44 | 27.09 | 80.51 | |
4 | 14 | 59 | 25 | 56 | −7 | 96.28 | 23.79 | 84.20 | |
5 | 14 | 59 | 24 | 51 | 0 | 96.25 | 23.00 | 85.09 | |
6 | 14 | 59 | 23 | 52 | 1 | 96.14 | 22.23 | 85.90 | |
7 | 14 | 59 | 22 | 51 | −6 | 96.06 | 21.47 | 86.71 | |
8 * | 14 | 59 | 21 | 50 | −6 | 95.72 | 20.74 | 87.29 | |
9 | −11 | 59 | 22 | 46 | 5 | 94.94 | 20.88 | 86.50 | |
10 | −11 | 73 | 20 | 44 | −7 | 92.50 | 20.10 | 85.45 | |
11 | 14 | 72 | 20 | 39 | 0 | 92.00 | 20.35 | 84.76 | |
12 | 14 | 75 | 17 | 37 | 4 | 90.78 | 19.75 | 84.48 | |
Scenario 2 | 13 | −7 | 66 | 31 | 60 | −6 | 95.94 | 26.27 | 81.07 |
14 | −7 | 66 | 29 | 60 | −1 | 95.78 | 24.37 | 83.13 | |
15 | −7 | 66 | 28 | 60 | −3 | 95.69 | 23.43 | 84.15 | |
16 | 9 | 66 | 27 | 60 | 1 | 95.53 | 22.49 | 85.10 | |
17 * | 9 | 65 | 26 | 59 | −2 | 94.97 | 21.54 | 85.76 | |
18 | −7 | 66 | 25 | 60 | 0 | 93.39 | 20.64 | 85.53 | |
19 | 9 | 79 | 18 | 37 | 2 | 84.06 | 20.10 | 78.69 | |
Scenario 3 | 20 | −6 | 52 | 31 | 59 | 9 | 95.25 | 29.03 | 77.32 |
21 | −6 | 52 | 30 | 59 | 7 | 94.81 | 28.18 | 77.95 | |
22 | 8 | 52 | 30 | 59 | −10 | 94.81 | 28.18 | 77.95 | |
23 | 8 | 52 | 29 | 59 | −4 | 94.72 | 27.33 | 78.86 | |
24 * | 8 | 52 | 28 | 59 | −19 | 94.64 | 26.50 | 79.76 | |
25 | −6 | 52 | 27 | 59 | −17 | 91.72 | 25.67 | 78.38 | |
26 | −5 | 51 | 26 | 56 | −9 | 91.31 | 25.14 | 78.67 | |
27 | 2 | 51 | 26 | 58 | 4 | 89.02 | 24.43 | 77.65 | |
28 | 8 | 79 | 18 | 41 | 9 | 80.44 | 19.72 | 76.23 |
Workspace | VP ID | X (m) | Y (m) | Z (m) | (°) | (°) | Coverage (%) | Distance (m) | F (%) |
---|---|---|---|---|---|---|---|---|---|
WS1 | VP1 | 51 | 39 | 11 | 30 | −1 | 100.00 | 22.30 | 87.34 |
VP2 | 53 | 12 | 13 | 39 | 12 | 100.00 | 22.41 | 87.23 | |
VP3 | 53 | 30 | 20 | 57 | 2 | 100.00 | 22.77 | 86.89 | |
WS2 | VP4 | 14 | 59 | 21 | 50 | −6 | 95.72 | 20.74 | 87.29 |
VP5 | −11 | 59 | 22 | 46 | 5 | 94.94 | 20.88 | 86.50 | |
VP6 | −11 | 73 | 20 | 44 | −7 | 92.50 | 20.10 | 85.45 | |
WS3 | VP7 | −12 | 41 | 23 | 48 | 1 | 90.41 | 21.49 | 82.16 |
VP8 | 15 | 35 | 22 | 48 | 0 | 90.40 | 21.63 | 82.00 | |
VP9 | 15 | 41 | 25 | 47 | −1 | 92.01 | 23.69 | 80.91 | |
WS4 | VP10 | 46 | −1 | 13 | 37 | 2 | 100.00 | 21.40 | 88.77 |
VP11 | 68 | −13 | 14 | 39 | −4 | 100.00 | 21.84 | 88.32 | |
VP12 | 53 | −14 | 20 | 50 | 0 | 100.00 | 22.29 | 87.86 |
Time Period | Path | (s) | (s) | (s) | (s) | (s) |
---|---|---|---|---|---|---|
VP1 –VP4 –VP1 | 4.15 | 4.66 | 5.93 | 111.09 | 117.02 | |
VP3 –VP12 –VP4 –VP3 | 5.04 | 5.58 | 10.97 | 105.88 | 116.85 | |
VP3 –VP12 –VP8 –VP3 | 5.04 | 5.58 | 9.37 | 107.48 | 116.85 |
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Huang, Y.; Hammad, A. Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities. Remote Sens. 2024, 16, 2445. https://doi.org/10.3390/rs16132445
Huang Y, Hammad A. Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities. Remote Sensing. 2024; 16(13):2445. https://doi.org/10.3390/rs16132445
Chicago/Turabian StyleHuang, Yusheng, and Amin Hammad. 2024. "Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities" Remote Sensing 16, no. 13: 2445. https://doi.org/10.3390/rs16132445
APA StyleHuang, Y., & Hammad, A. (2024). Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities. Remote Sensing, 16(13), 2445. https://doi.org/10.3390/rs16132445