Abstract
Abstract— In this paper, we present a unique and efficient
visible trajectory planning for aerial swarm using
decentralized algorithms in a 3D urban environment. By using
SwarmLab environment, we are comparing two decentralized
algorithms from the state of the art for the navigation of aerial
swarms, Olfati-Saber’s and Vasarhelyi’s. The first step in our
concept is to extract basic geometric shapes. We focus on three
basic geometric shapes from point clouds in urban scenes that
can appear: planes, cylinders and spheres, extracting these
geometric shapes using efficient Random Sample Consensus
(RANSAC) algorithms with a high success rate of detection.
The second step is a decentralized swarm algorithm for motion
planning, demonstrated on drones in urban environment. Our
planner includes dynamic and kinematic platform’s limitation,
generating visible trajectories based on our first step
mentioned earlier. We demonstrate our visibility and
trajectory planning method in simulations, showing trajectory
planning in 3D urban environments for drone’s swarm with
decentralized algorithms demonstrating performance analysis
,such as order, safety, connectivity and union.
visible trajectory planning for aerial swarm using
decentralized algorithms in a 3D urban environment. By using
SwarmLab environment, we are comparing two decentralized
algorithms from the state of the art for the navigation of aerial
swarms, Olfati-Saber’s and Vasarhelyi’s. The first step in our
concept is to extract basic geometric shapes. We focus on three
basic geometric shapes from point clouds in urban scenes that
can appear: planes, cylinders and spheres, extracting these
geometric shapes using efficient Random Sample Consensus
(RANSAC) algorithms with a high success rate of detection.
The second step is a decentralized swarm algorithm for motion
planning, demonstrated on drones in urban environment. Our
planner includes dynamic and kinematic platform’s limitation,
generating visible trajectories based on our first step
mentioned earlier. We demonstrate our visibility and
trajectory planning method in simulations, showing trajectory
planning in 3D urban environments for drone’s swarm with
decentralized algorithms demonstrating performance analysis
,such as order, safety, connectivity and union.
| Original language | English |
|---|---|
| Title of host publication | GEOProcessing 2022 |
| Subtitle of host publication | The Fourteenth International Conference on Advanced Geographic Information Systems, Applications, and Services |
| State | Published - 2022 |
| Externally published | Yes |