Indoor Path-Planning Algorithm for UAV-Based Contact Inspection
2.1. Point Cloud Pre-Processing
2.1.1. Room Segmentation
|Algorithm 1 Door Location Algorithm|
2.1.2. Point Cloud Pre-Processing
- Empty: Voxels that do not contain points inside.
- Occupied: Voxels that contain points inside.
- Security-offset: Empty voxels that are near an occupied one, and therefore, are not navigable by the drone.
- Exterior: Empty voxels that are outside the room.
2.1.3. Navigation Map Generation
- Target voxel is the empty voxel that contains the door position and labeled with a “1”.
- Navigable voxels are empty voxels directly connected to the target voxel, meaning that there is at least one path from the voxel to the target. The label of these voxels depends on the number of surrounding voxels and the distance to the target.
- Nonnavigable voxels are empty voxels that are not directly connected to the target voxel, meaning that there is no possible path between them and the target voxel. These voxels are given a label value of “−1”.
- Common face: Voxels surrounding the study voxels that have a common face. This means that the surrounding voxels and the study one have two coincident indices.
- Common edge: Voxels surrounding the study voxel that have a common edge. This means that the surrounding voxels and the study one have one coincident index.
- Common vertex: Voxels surrounding the study voxel that have a common vertex. This means that the surrounding voxels and the study voxel do not have any coincident indices.
- The route cannot pass through the same room twice.
- If two or more routes pass through the same room, the one containing the lower number of rooms is selected as the best route.
- If two routes are valid and do not share any part of the path, that containing the lowest number of waypoints, i.e., the shortest path, is selected.
2.2. Path Planning
|Algorithm 2 Path Planning Algorithm|
3. Result and Discussion
3.1. Case Study
- The pre-processing step segments the entire point cloud into rooms, and each room is discretized, calculating the navigation map for each one using the doors as the target position.
- The algorithm calculates the navigation graph of the building where the system is going to navigate.
- The path planning algorithm uses the navigation graph to calculate the rooms that the UAV has to cross in order to go from an initial point in one room to a final point in another one.
- The path planner algorithm also uses the navigation map generated in the pre-processing step to calculate the path, making this calculation in real time.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|N. Path||Initial Pos. (m)||Final Pos. (m)||Vec. Dir.||Time (s)||N. Waypoints.|
|1||[−27.9, 5.85, −0.8]||[4.2, 2.3, 1]||[−1, 0, 0]||0.008745||345|
|2||[−13.5, 5.85, −0.8]||[19.4, 2.3, 1]||[−1, 0, 0]||0.006259||340|
|3||[1.2, 5.85, −0.8]||[−10.6, 2.3, 1]||[−1, 0, 0]||0.00851||342|
|4||[17.2, 5.85, −0.8]||[−25.5, 2.3, 1]||[−1, 0, 0]||0.012885||263|
|5||[32, 5.85, −0.8]||[−40.6, 2.3, 1]||[−1, 0, 0]||0.006533||415|
|6||[−27.9, 5.85, −0.8]||[18.4, 2.5, 1]||[1, 0, 0]||0.008942||303|
|7||[−13.5, 5.85, −0.8]||[33.5, 3, 1]||[1, 0, 0]||0.005645||289|
|8||[17.2, 5.85, −0.8]||[3.4, 3, 1]||[1, 0, 0]||0.009818||151|
|9||[17.2, 5.85, −0.8]||[−11.5, 3, 1]||[1, 0, 0]||0.005481||239|
|10||[32, 5.85, −0.8]||[−26.7, 3, 1]||[1, 0, 0]||0.006902||377|
|N. Path||Time A* (s)||Time Modified A* (s)|
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González de Santos, L.M.; Frías Nores, E.; Martínez Sánchez, J.; González Jorge, H. Indoor Path-Planning Algorithm for UAV-Based Contact Inspection. Sensors 2021, 21, 642. https://doi.org/10.3390/s21020642
González de Santos LM, Frías Nores E, Martínez Sánchez J, González Jorge H. Indoor Path-Planning Algorithm for UAV-Based Contact Inspection. Sensors. 2021; 21(2):642. https://doi.org/10.3390/s21020642Chicago/Turabian Style
González de Santos, Luis Miguel, Ernesto Frías Nores, Joaquín Martínez Sánchez, and Higinio González Jorge. 2021. "Indoor Path-Planning Algorithm for UAV-Based Contact Inspection" Sensors 21, no. 2: 642. https://doi.org/10.3390/s21020642