Swarm of micro flying robots in the wild
Abstract
Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.
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- Authors
- Zhou, X., Wen, X., Wang, Z., Gao, Y., Li, H., Wang, Q., Yang, T., Lu, H., Cao, Y., Xu, C., & Gao, F.
- Affiliations
- State Key Laboratory of Industrial Control Technology (CN)
- Hong Kong University of Science and Technology (HK)
- Zhejiang University (CN)
- Corresponding author
- Xu, Chao
- Gao, Fei
- ORCID
- Venue
- Science Robotics
- Published
- 2022-05-25
- Volume
- 7
- Issue
- 66
- Pages
- eabm5954-eabm5954
- ISSN
- 2470-9476 (ISSN-L)
- OpenAlex
- W4229007520
- Semantic Scholar
- 11c762b2f0b22273a0fde8498eb5349fcd38e1d7
- Cited by
- 608
- References
- 52
- Citations by year
- 2026: 122 · 2025: 206 · 2024: 142 · 2023: 112 · 2022: 26
- FWCI
- 39.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science Robotics (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- 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
- UAV swarms, trajectory planning, cluttered environments, dense forest, obstacle avoidance, swarm coordination, autonomous drones, field experiments
- MeSH terms
- Algorithms, Humans, Robotics, Speech Disorders, Sports, Benchmarking
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