RotorS—A Modular Gazebo MAV Simulator Framework

By Fadri Furrer, Michael Burri, Markus Achtelik, Roland Yves Siegwart. 2016

517 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering, Robotics & Control

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Citations in the corpus

A General Framework for Uncertainty Estimation in Deep Learning (2019, methodology): “We collect annotated training data to predict elin, eang in simulation [38].”
Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, methodology): “We use RotorS [10] and Gazebo for all simulation experiments.”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, methodology): “Similarly, RotorS [7] provides a modular framework to design Micro Aerial Vehicles, and build algorithms for control and state estimation that can be tested in simulator.”
Learning high-speed flight in the wild (2021, methodology): “We perform all experiments in the Flightmare simulator [33] using the RotorS [34] Gazebo plugin for accurate physics modeling and Unity as a rendering engine [35].”
Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop (2018, background): “In robotics, Gazebo [16] is the ubiquitous full simulation environment, with speciﬁc applications to UAVs in RotorS [17], which is studied in depth in [18].”
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “…Servoing through Reinforcement Learning component (from now on RL-IBVS) is based on our own designed reinforcement learning framework which integrates an agent (i.e. an aerial robot executing a deep RL algorithm) which can interact with an environment based on RotorS Gazebo [20] simulator (see Fig.”
Data-Driven MPC for Quadrotors (2021, methodology): “We employ the AscTec Hummingbird quadrotor model using the RotorS extension [36].”
A framework for maximum likelihood parameter identification applied on MAVs (2018, methodology): “In our application, these equations are theMAV dynamic model defined in (5) or IMUmodel (6).”
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (2017, unknown): “JMAVSim, [44; 52] and RotorS by [11]).”
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments (2019, methodology): “the Gazebo-based RotorS simulator [26] accurately models the MAV’s physics.”
Synthetic Data for Deep Learning (2019, methodology): “Gazebo has been used for UAV simulations [183], but there are more popular specialized simulators in the field.”
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation (2017, methodology): “Examples include JMAVSim [36, 44] which was used to develop and evaluate controllers and RotorS [7] which was used to study visual servoing.”