3D Trajectory Planning Method for UAVs Swarm in Building Emergencies
Abstract
:1. Introduction
- Task Planning: For a set of vehicles and set of destinations, the most optimal vehicle-objective combination is established, considering aspects such as the speed and endurance of the vehicle, the distance to the goal, and the task to be undertaken [13].
- Trajectory Planning (or required movements): Once the objective has been assigned, safe and efficient paths are generated for one or more vehicles to be able capable of reaching this location [14].
2. Related Works
2.1. 3D Path Planning
2.2. Emergency Drones Applications
3. 3D Multi-Trajectory Planning Based on PRM
3.1. 3D Occupancy Map Generation
3.2. Exploration of the 3D Environment Based on PRM
Algorithm 1: 3D probabilistic roadmaps. |
Input: 3D Occupancy Map Output: 3D Probabilistic Roadmap (Figure 3). |
- : A file containing the initial positions of the vehicles. In this case, these positions are established concerning (0,0,0) of Gazebo. In the real application, the conversion of the global coordinates to a local reference system is required taking into account the initial position of one of the vehicles. Knowing the position of the goals and each of the vehicles, it is possible to establish a common reference system for all agents involved in the mission.
- : A file containing the final positions of the goals to be achieved. As in the previous case, these coordinates appear in meters with respect to the coordinates of the origin simulation. As indicated, the idea is to introduce intermediate processing that is in charge of taking the position of each vehicle and the locations to reach, referencing all data to a common reference system.
- , , , , , : Sets of the maximum and minimum values of the coordinate of a node in the X, Y, and Z axes, respectively. These values are fixed taking into account the size of the Octree, and they are extracted through functions collected in the Octomap library.
- : Variable that allows parameterizing the number of nodes to explore the environment. As mentioned above, it is important to establish a balance between computing time and a high possibility of finding all possible solutions. The greater is the number of nodes, the longer is the computation time, but the greater are the chances of finding a solution and making it as short as possible. If the number of nodes is decreased, the computation time is minimized, but the possibility of finding a possible solution is reduced.
- , : To improve the creation of the PRM, these values are introduced in such a way that the generated edges are in a range of distance. That leads to eliminate the path of two very close nodes. The maximum distance is limited to increase the possibility to find a free collision path. In the case of the number of nodes, it is necessary to establish a balance between these distances and the total size of the Octree.
- Generation of nodes: Nodes are generated with random coordinate values, as shown on Lines 8–10 of Algorithm 1. To do this, it is necessary to use the variable , which corresponds to a constant of C++ that returns the maximum value of the function . Once the node is generated, the next step is to look for this node inside the Octree, and check if it corresponds to free space. If so, it is added to the set of PRM nodes.
- Edge generation: The next step is to establish all possible connections between the generated nodes. To achieve this, first the algorithm checks if the nodes are at a valid distance according to the established range, and then verifies if the line connects both nodes is free collision path. This process is carried out using the Octomap libraries, which have functions that allow throwing rays from one point to another, receiving if the ray is hitting an obstacle or not, and checking that the end of the ray is the destination and not an intermediate point. In addition, this function takes into account the direction in which the beam must be launched and its distance, thus it ensures that the path sought is between the two nodes generated. Once the check is done, the edge is saved.
3.3. Generation of Paths
3.3.1. Generation of Paths for the Labeled Case
3.3.2. Generation of Paths for the Unlabeled Case
3.4. Collision Avoidance
Algorithm 2: Collision avoidance between UAVs. |
|
4. Simulation and Results
4.1. Control Architecture and Simulation
4.2. Results
- PRM Average: It is the average computational time for generating PRM without considering if it is labeled or unlabeled case.
- Labeled Case Average: It is the average computational time for generating the solution for the labeled case.
- Unlabeled Case Average: It is the average computational time for generating the solution for the unlabeled case.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Safety Margin as UAV Size (m) | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 |
---|---|---|---|---|---|---|---|---|---|---|
0.5 | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
1.0 | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
1.5 | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
2.0 | Y | Y | Y | N | Y | Y | Y | Y | Y | Y |
2.5 | Y | Y | Y | N | Y | Y | Y | N | Y | Y |
3.0 | Y | Y | N | N | Y | Y | Y | N | Y | Y |
3.5 | N | Y | N | N | Y | N | N | N | Y | N |
4.0 | N | N | N | N | Y | N | N | N | N | N |
4.5 | N | N | N | N | Y | N | N | N | N | N |
5.0 | N | N | N | N | N | N | N | N | N | N |
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Madridano, Á.; Al-Kaff, A.; Martín, D.; de la Escalera, a.A. 3D Trajectory Planning Method for UAVs Swarm in Building Emergencies. Sensors 2020, 20, 642. https://doi.org/10.3390/s20030642
Madridano Á, Al-Kaff A, Martín D, de la Escalera aA. 3D Trajectory Planning Method for UAVs Swarm in Building Emergencies. Sensors. 2020; 20(3):642. https://doi.org/10.3390/s20030642
Chicago/Turabian StyleMadridano, Ángel, Abdulla Al-Kaff, David Martín, and and Arturo de la Escalera. 2020. "3D Trajectory Planning Method for UAVs Swarm in Building Emergencies" Sensors 20, no. 3: 642. https://doi.org/10.3390/s20030642