Performance benchmarking of quadrotor systems using time-optimal control

Hehn, Markus; Ritz, Robin; D’Andrea, Raffaello

doi:10.1007/s10514-012-9282-3

Cited by 86 publications

(88 citation statements)

References 18 publications

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“…Often, the objective function will be to minimize time of traversal, energy usage, or some penalty weighting the relative costs of deviation from a goal state and the use of control inputs (i.e., LQR). Recently, these tools have been applied to generate extremely dynamic maneuvers for quadrotor helicopters [22] in motion-capture environments. For comparison, Barry et al report 3-5 minutes of computation time [5] to optimize a 4.5 m trajectory for a 12-state, 5-input airplane maneuvering between several cylindrical obstacles using direct collocation with the SNOPT optimization software [4].…”

Section: Trajectory Planningmentioning

confidence: 99%

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Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

Bry

Richter

Bachrach

et al. 2015

The International Journal of Robotics Research

120

105

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In this paper, we describe trajectory planning and state estimation algorithms for aggressive flight of micro aerial vehicles in known, obstacledense environments. Finding aggressive but dynamically feasible and collision-free trajectories in cluttered environments requires trajectory optimization and state estimation in the full state space of the vehicle, which is usually computationally infeasible on realistic time scales for real vehicles and sensors. We first build on previous work of van Nieuwstadt and Murray [51] and Mellinger and Kumar [38], to show how a search process can be coupled with optimization in the output space of a differentially flat vehicle model to find aggressive trajectories that utilize the full maneuvering capabilities of a quadrotor. We further extend this work to vehicles with complex, Dubins-type dynamics and present a novel trajectory representation called a Dubins-Polynomial trajectory, which allows us to optimize trajectories for fixed-wing vehicles. To provide accurate state estimation for aggressive flight, we show how the Gaussian particle filter can be extended to allow laser range finder localization to be combined with a Kalman filter. This formulation allows similar estimation accuracy to particle filtering in the full vehicle state but with an order of magnitude more efficiency. We conclude with experiments demonstrating the execution of quadrotor and fixed-wing trajectories in cluttered environments. We show results of aggressive flight at speeds of up to 8 m/s for the quadrotor and 11 m/s for the fixed-wing aircraft.

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Section: Trajectory Planningmentioning

confidence: 99%

“…Since the IMU provides accurate measurements of ω b and a b , we follow the commonly-used technique of omitting ω b from the state, neglecting equation 26, and treating the IMU measurements as inputs to the filter using a standard linearized IMU update. For the quantities used in equation 22 we have…”

Section: Process Equationsmentioning

confidence: 99%

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

Bry

Richter

Bachrach

et al. 2015

The International Journal of Robotics Research

120

105

View full text Add to dashboard Cite

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“…The effectiveness of this approach was shown shown in terms of real-time interception maneuvers [6] and coordinated ball throwing and catching [7]. An indirect optimal control method is applied to the minimum time problem in [8].…”

Section: A Related Workmentioning

confidence: 99%

Learning quadrotor maneuvers from optimal control and generalizing in real-time

Tomić

Maier

Haddadin

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.

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“…The work in [17] constructed a dynamically feasible, desired speed profile for a given sequence of waypoints. The authors in [18,19] worked on trajectory generation for quadrotors by implementing dynamic constraints to an optimal control method and verified the existence of optimal trajectories. The work in [20] addresses the problem of quadrotor trajectory generation and tracking while carrying a suspended payload.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

Subbarao

2017

Aerospace

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This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances.

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Performance benchmarking of quadrotor systems using time-optimal control

Cited by 86 publications

References 18 publications

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

Learning quadrotor maneuvers from optimal control and generalizing in real-time

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

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