A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation

By Mark Wilfried Mueller, Markus Hehn, Raffaello D'Andrea. 2015

367 citations recorded by the source corpus.

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

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Citations in the corpus

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018, methodology): “While these methods can be used to perform visual teach and repeat [4], they are not concerned with trajectory generation [16], [20].”
Vision-based autonomous quadrotor landing on a moving platform (2017, methodology): “Such a cost is the integral of the jerk along the trajectory, which the authors of [26] show to be an upper bound on the product of the inputs to the vehicle, namely the collective thrust and the angular velocities around the three body axes.”
Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision (2016, methodology): “To do so, we use the approach proposed in [11], where a fast method to generate feasible trajectories for flying robots is presented.”
Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles (2021, background): “Typical examples are spline motion primitives [3] and optimization-based spline planning [4].”
Crazyswarm: A large nano-quadcopter swarm (2017, methodology): “Our solution is similar to the motion primitive described in [19], but we constrain .”
PAMPC: Perception-Aware Model Predictive Control for Quadrotors (2018, methodology): “In this work, we use the dynamical model of a quadrotor proposed in [3]:”
Thrust Mixing, Saturation, and Body-Rate Control for Accurate Aggressive Quadrotor Flight (2017, background): “Methods for checking the feasibility of the entire trajectory after generation are proposed in [15] and [16] by simplifying the problem with conservative approximations which do not allow Fig.”
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight (2019, methodology): “We compute a closed form trajectory that minimizes J ( T ) from x c to the goal state x g by applying the Pontryagins minimum principle [16]: where p µc , v µc , p µg , v µg are the current and goal position and velocity.”
Dynamic obstacle avoidance for quadrotors with event cameras (2020, methodology): “Rapid methods to compute motion primitives for aerial vehicles exist in the literature ( 48 ).”