Minimum snap trajectory generation and control for quadrotors

By Daniel Mellinger, Vijay R. Kumar. 2011

2,388 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Robotics & Control

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Citations in the corpus

A General Framework for Uncertainty Estimation in Deep Learning (2019, methodology): “We deﬁne the common model of a quadrotor, e.g. the one of Mellinger et al. [37], as the nominal model.”
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].”
Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, methodology): “This makes it extremely difficult to fully exploit the properties of popular minimum-snap or minimum-jerk trajectories [19, 20] for small, agile quadrotors using only onboard sensing and computing.”
Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories (2017, background): “, in [2], in which the linear rotor drag components are typically neglected, i.”
Learning monocular reactive UAV control in cluttered natural environments (2012, background): “Several state-of-the-art approaches for MAV control would be ideal to fly through a forest, as they feature impressive aggressive maneuvers [13] or can even be used for formation flight with large swarms of MAVs [14].”
Comparison of Trajectory Optimization Algorithms for High-Speed Quadrotor Flight Near Obstacles (2018, methodology): “There have been numerous impressive demonstrations using this type of algorithm, including flight through narrow vertical windows [4], [6] and through restrictive indoor environments [1], [7], [8].”
A computationally efficient algorithm for state-to-state quadrocopter trajectory generation and feasibility verification (2013, background): “A second group of algorithms exploits the differential flatness of the quadrocopter dynamics in order to derive constraints on the trajectory, and then solves an optimization problem over a class of trajectories, for example minimum snap [10], minimum time [11], shortest path under uncertain conditi”
State estimation for aggressive flight in GPS-denied environments using onboard sensing (2012, background): “Recent work has demonstrated systems that can perform impressive acrobatics [5] and other control feats [15], [16], however such systems are completely reliant on extremely accurate state estimates provided by external camera arrays.”
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight (2019, methodology): “Hard-constrained methods are pioneered by minimum-snap trajectory generation [1], in which trajectories are represented as piecewise polynomials and generated by solving a quadratic programming(QP) problem.”
Nonlinear robust tracking control of a quadrotor UAV on SE(3) (2011, neutral): “Tracking control of a quadrotor UAV has been considered in [19, 20], but the control system in [19] has a complex structure since it is based on a multiple-loop backstepping Manuscript received April 26, 2011; revised October 27, 2011; accepted May 3, 2012.”
Trajectory generation and control for precise aggressive maneuvers with quadrotors (2012, methodology): “Each controller is tuned automatically to obtain the desired performance using a combination of off-line system identiﬁcation techniques and on-line parameter identiﬁcation techniques (Landau et al. 1998; Mellinger et al. 2011).”
Swarm of micro flying robots in the wild (2022, methodology): “Trajectory representation Here, spatial-temporal trajectory planning is achieved using a newly developed trajectory representation named MINCO (minimum control) (56) that is designed for differentially flat systems like multicopters (4).”
Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments (2017, methodology): “In our optimization process, we use a sample-based method to confine each polynomial, the details for which can be found in [2].”
Towards a swarm of agile micro quadrotors (2012, methodology): “As described in Mellinger and Kumar (2011) an optimization program can be used to generate trajectories that smoothly transition through n w desired waypoints at speciﬁed times, t w .”
FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning (2020, background): “The former is pioneered by minimum-snap trajectory [19], whose closed-form solution was presented [20] later.”
Vision-Only Robot Navigation in a Neural Radiance World (2021, background): “Notably, quadrotors are known to be differentially ﬂat, with their position and yaw angle as ﬂat outputs [28].”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, methodology): “These optimizations scheme can be made even more efﬁcient for the class of differentially ﬂat systems by using splines for the state trajectories [24, 30, 44].”
Learning high-speed flight in the wild (2021, methodology): “This representation enforces continuity in position, velocity, and acceleration with respect to the current state, and facilitates dynamic feasibility because of differential flatness [43].”
RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight (2020, methodology): “Hard-constrained methods are pioneered by minimum-snap trajectory [2], in which piecewise polynomial trajectories are generated through quadratic programming(QP).”