Trajectory Planning for Quadrotor Swarms
Abstract
We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.
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Space agencies such as NASA and ESA have started to show interest in swarm technologies, with activities such as the Swarmathon competition and research on control of swarms of picosatellites.
“Accordingly, space agencies, such as NASA and ESA, have started to show interest in swarm technologies, for example, with activities such as the already mentioned Swarmathon competition [69] and with research directed at the control of swarms of picosatellites [130].”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [130]
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Swarm of micro flying robots in the wild
Zhou, X., Wen, X., Wang, Z., Gao, Y., Li, H., Wang, Q., Yang, T., Lu, H., Cao, Y., Xu, C., et al. (2022) · Science Robotics · doi:10.1126/scirobotics.abm5954 · Cited by 608 · see citing works
Swarm Robotics: Past, Present, and Future [Point of View]
Dorigo, M., Theraulaz, G., & Trianni, V. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3072740 · Cited by 401 · see citing works
- Authors
- Honig, W., Preiss, J. A., Kumar, T. K. S., Sukhatme, G. S., & Ayanian, N.
- Affiliations
- University of Southern California (US)
- Venue
- IEEE Transactions on Robotics
- Published
- 2018-08
- Volume
- 34
- Issue
- 4
- Pages
- 856-869
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2886374915
- Cited by
- 315
- References
- 0
- Citations by year
- 2026: 24 · 2025: 49 · 2024: 51 · 2023: 33 · 2022: 42 · 2021: 56 · 2020: 29 · 2019: 28 · 2018: 3
- FWCI
- 14.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Funders
- ONR (N00014-16-1-2907, N00014-14-1-0734)
- ARL (W911NF-14-D-0005)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Keywords
- quadrotor swarms, downwash effect, warehouse navigation, obstacle-rich environments
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