Differential Flatness Transformations for Aggressive Quadrotor Flight

Morrell, Benjamin; Rigter, Marc; Merewether, Gene; Reid, Robert; Thakker, Rohan; Tzanetos, Theodore; Rajur, Vinay; Chamitoff, Gregory

doi:10.1109/icra.2018.8460838

Cited by 22 publications

(18 citation statements)

References 20 publications

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“…Dr. Scaramuzza received an SNSF-ERC Starting Grant, the IEEE Robotics and Automation Early Career Award, and a Google Research Award for his research contributions. 16 Fig. 16: Visualization of network attention using the Grad-CAM technique [55].…”

Section: Discussionmentioning

confidence: 99%

“…From the control perspective, plenty of work has been done to enable high-speed navigation, both in the context of autonomous drones [7], [15], [16] and autonomous cars [17]- [20]. However, the inherent difficulties of state estimation make these methods difficult to adapt for small, agile quadrotors that must rely solely on onboard sensing and computing.…”

Section: Related Workmentioning

confidence: 99%

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Deep Drone Racing: From Simulation to Reality With Domain Randomization

et al. 2020

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Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform-and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Drone Racing: From Simulation to Reality With Domain Randomization

et al. 2020

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“…The differential flatness property of multirotors has been widely exploited for accurate trajectory tracking [11,17,28,12,34,30]. Differential flatness of the multirotor subject to linear drag was shown in Faessler et al [11].…”

Section: B Related Workmentioning

confidence: 99%

Inverting Learned Dynamics Models for Aggressive Multirotor Control

Spitzer¹,

Michael²

2019

Robotics: Science and Systems XV

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We present a control strategy that applies inverse dynamics to a learned acceleration error model for accurate multirotor control input generation. This allows us to retain accurate trajectory and control input generation despite the presence of exogenous disturbances and modeling errors. Although accurate control input generation is traditionally possible when combined with parameter learning-based techniques, we propose a method that can do so while solving the relatively easier non-parametric model learning problem. We show that our technique is able to compensate for a larger class of model disturbances than traditional techniques can and we show reduced tracking error while following trajectories demanding accelerations of more than 7 m/s 2 in multirotor simulation and hardware experiments.

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“…NASA's JPL lab report in their research results that their drone can finish their race track in a similar amount of time as a professional pilot. In their research, a visual‐inertial localization and mapping system is used for navigation and an aggressive trajectory connecting waypoints is generated to finish the track (Morrell et al, 2018). Gao et al (2019) come up with a teach‐and‐repeat solution for drone racing.…”

Section: Introductionmentioning

confidence: 99%

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

Horst

Duernay

et al. 2020

Journal of Field Robotics

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Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model-predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan," which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones. K E Y W O R D S autonomous drone race, visual model-predictive localization

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Differential Flatness Transformations for Aggressive Quadrotor Flight

Cited by 22 publications

References 20 publications

Deep Drone Racing: From Simulation to Reality With Domain Randomization

Deep Drone Racing: From Simulation to Reality With Domain Randomization

Inverting Learned Dynamics Models for Aggressive Multirotor Control

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

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