See the published version of this preprint at Nature Machine Intelligence.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Article
Enrica Soria
EPFL
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Classical models of aerial swarms often describe global coordinated motion as the combination of local interactions that happen at the individual level. Mathematically, these interactions are represented with Potential Fields. Despite their explanatory success, these models fail to guarantee rapid and safe collective motion when applied to aerial robotic swarms flying in cluttered environments of the real world, such as forests and urban areas. Moreover, these models necessitate a tight coupling with the deployment scenarios to induce consistent swarm behaviors. Here, we propose a predictive model that combines the local principles of potential field models with the knowledge of the agentsβ dynamics. We show that our approach improves the speed, order, and safety of the swarm, it is independent of the environment layout, and scalable in the swarm speed and inter-agent distance. Our model is validated with a swarm of five quadrotors that can successfully navigate in a real-world indoor environment populated with obstacles.
Keywords
aerial swarms, cluttered environments, potential fields models, predictive model
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- movies1.mp4
Simulation Experiments
- movies2.mp4
Hardware Experiments
- supplementarymaterials.pdf
Supplementary Materials
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Nature Machine Intelligence.
Version 1
Posted 29 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
See the published version of this preprint at Nature Machine Intelligence.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Article
Enrica Soria
EPFL
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Nature Machine Intelligence.
Version 1
Posted 29 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Nature Machine Intelligence.
Classical models of aerial swarms often describe global coordinated motion as the combination of local interactions that happen at the individual level. Mathematically, these interactions are represented with Potential Fields. Despite their explanatory success, these models fail to guarantee rapid and safe collective motion when applied to aerial robotic swarms flying in cluttered environments of the real world, such as forests and urban areas. Moreover, these models necessitate a tight coupling with the deployment scenarios to induce consistent swarm behaviors. Here, we propose a predictive model that combines the local principles of potential field models with the knowledge of the agentsβ dynamics. We show that our approach improves the speed, order, and safety of the swarm, it is independent of the environment layout, and scalable in the swarm speed and inter-agent distance. Our model is validated with a swarm of five quadrotors that can successfully navigate in a real-world indoor environment populated with obstacles.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- movies1.mp4
Simulation Experiments
- movies2.mp4
Hardware Experiments
- supplementarymaterials.pdf
Supplementary Materials
Loading...