RotorS—A Modular Gazebo MAV Simulator Framework
Open accessAbstract
In this chapter we present a modular Micro Aerial Vehicle (MAV) simulation framework, which enables a quick start to perform research on MAVs. After reading this chapter, the reader will have a ready to use MAV simulator, including control and state estimation. The simulator was designed in a modular way, such that different controllers and state estimators can be used interchangeably, while incorporating new MAVs is reduced to a few steps. The provided controllers can be adapted to a custom vehicle by only changing a parameter file. Different controllers and state estimators can be compared with the provided evaluation framework. The simulation framework is a good starting point to tackle higher level tasks, such as collision avoidance, path planning, and vision based problems, like Simultaneous Localization and Mapping (SLAM), on MAVs. All components were designed to be analogous to its real world counterparts. This allows the usage of the same controllers and state estimators, including their parameters, in the simulation as on the real MAV.
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- Authors
- Furrer, F., Burri, M., Achtelik, M., & Siegwart, R.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Furrer, Fadri
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 595-625
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W2465948386
- Cited by
- 592
- References
- 7
- Citations by year
- 2026: 24 · 2025: 50 · 2024: 69 · 2023: 83 · 2022: 79 · 2021: 81 · 2020: 77 · 2019: 55 · 2018: 37 · 2017: 27 · 2016: 10
- FWCI
- 117.99
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Studies in computational intelligence (publishedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- micro air vehicles, Gazebo, modular simulation framework, state estimation, controller performance evaluation, collision avoidance, path planning, SLAM
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