Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor

By Robert E. Mahony, Vijay R. Kumar, Peter Corke. 2012

1,541 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, methodology): “0 parameters from [44] and the classical quadrotor motion model from [45] to analytically compute those two quantities.”
Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories (2017, background): “Rotor drag effects originate from blade flapping and induced drag of the rotors, which are, thanks to their equivalent mathematical expression, typically combined as linear effects in a lumped parameter dynamical model [6].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, positive): “Usually this is done by having several nested control loops for position, velocity, attitude and angular velocity, as summarized by Mahony et al. (2012).”
Adaptive Incremental Nonlinear Dynamic Inversion for Attitude Control of Micro Air Vehicles (2016, methodology): “Typically, the stabilization algorithm used for MAVs is simple proportional integral derivative (PID) control [1,2].”
Nonlinear control of VTOL UAVs incorporating flapping dynamics (2013, background): “Only recently have these aerodynamic drag forces been taken into account for the modeling of small-scale quadrotors [13], [10], [1], [9].”
Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments (2014, background): “The function principle of quadrotors can be found in [5], [6].”
Aerodynamics and Control of Quadrotors (2017, methodology): “Over the years, many shortcomings of this static free air model have been observed especially by those performing aggressive manoeuvres Mellinger and Kumar [2011]; Lupashin et al.”
Nonlinear feedback control of Quadrotors exploiting First-Order Drag Effects (2017, positive): “In Bristeau et al. (2009), Pounds et al. (2010), Bangura and Mahony (2012), Mahony et al. (2012) and Bangura et al. (2016), it has been considered that due to the natural ﬂexibility of the blades, small-scale hingeless rotors exhibit similar mechanical behavior to the one produced by hinged rotors…”
Automatic re-initialization and failure recovery for aggressive flight with a monocular vision-based quadrotor (2015, background): “Also note that by construction, the z component of qe,rp is always zero, which assures that no rotation around the body z axis is necessary to align êBz with e B z,des.”
Real-time dynamics of soft and continuum robots based on Cosserat rod models (2019, methodology): “Robot-focused works such as Rone and Ben-Tzvi (2014), Falkenhahn et al.”
A Novel Overactuated Quadrotor Unmanned Aerial Vehicle: Modeling, Control, and Experimental Validation (2015, background): “tion to allow for an effective and robust flight performance (see [3]–[5] for an overview).”
Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying (2017, neutral): “, [34], [35], [45], and [46] for more details).”
Robust state estimation for Micro Aerial Vehicles based on system dynamics (2015, methodology): “The source of those forces was found to be blade flapping and rotor drag forces caused by induced drag generated at the rotor blades [9].”
Relaxed hover solutions for multicopters: Application to algorithmic redundancy and novel vehicles (2016, background): “The inputs may also be based on symmetry properties of a specific vehicle, so that, for example, independent torques are used as inputs, from which the required propeller speeds are recovered (as is commonly done when controlling quadrocopters, e.g. Mahony et al. (2012)).”
Thrust Control for Multirotor Aerial Vehicles (2017, methodology): “…is a challenge and the airspeed measurements are unreliable for velocities under 1 m/s. Arain and Kendoul [3] also obtained errors of up to 2 m/s for ground truth forward velocity of 6 m/s. Yeo et al. [31] used their wind estimates in designing safe trajectories, though errors were observed of 0.4…”