Deep Drone Racing: From Simulation to Reality With Domain Randomization

Loquercio, Antonio; Kaufmann, Elia; Ranftl, René; Dosovitskiy, Alexey; Koltun, Vladlen; Scaramuzza, Davide

doi:10.1109/tro.2019.2942989

Cited by 193 publications

(130 citation statements)

References 47 publications

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“…Unmanned Aerial Vehicles (UAVs) are increasingly used for delivery, search and rescue, and inspection operations [1]- [4]. Quadrotor UAVs are a popular option for tasks that require highly agile maneuvers in complex, constrained, environments [5]. Control approaches that lead to robust and high performance operation of quadrotors are needed to execute tasks in a dependable manner.…”

Section: Introductionmentioning

confidence: 99%

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cohen

Abdulrahim

Forbes

2020

IEEE Robot. Autom. Lett.

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This paper considers optimal control of a quadrotor unmanned aerial vehicles (UAV) using the discrete-time, finite-horizon, linear quadratic regulator (LQR). The state of a quadrotor UAV is represented as an element of the matrix Lie group of double direct isometries, SE2(3). The nonlinear system is linearized using a left-invariant error about a reference trajectory, leading to an optimal gain sequence that can be calculated offline. The reference trajectory is calculated using the differentially flat properties of the quadrotor. Monte-Carlo simulations demonstrate robustness of the proposed control scheme to parametric uncertainty, state-estimation error, and initial error. Additionally, when compared to an LQR controller that uses a conventional error definition, the proposed controller demonstrates better performance when initial errors are large.

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Section: Introductionmentioning

confidence: 99%

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cohen

Abdulrahim

Forbes

2020

IEEE Robot. Autom. Lett.

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“…Besides that, specific autonomous navigation strategies for a racing drone are also discussed in the literature. Recent papers [24,36], and some previously commented [5,12,33] address methods of localization, control and planning for autonomous navigation in racing environments without obstacles.…”

Section: Solutions For Drone Racingmentioning

confidence: 99%

An integrated solution for an autonomous drone racing in indoor environments

Rezende

Miranda

Rezeck

et al. 2021

Intel Serv Robotics

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The use of drones is becoming more present in modern daily life. One of the most challenging tasks associated with these vehicles is the development of perception and autonomous navigation systems. Competitions such as Artificial Intelligence Robotic Racing (AIRR) and Autonomous Drone Racing were launched to drive the advances in such strategies, requiring the development of integrated systems for autonomous navigation in a racing environment. In this context, this paper presents an improved integrated solution for autonomous drone racing, which focuses on simple, robust, and computationally efficient techniques to enable the application in embedded hardware. The strategy is divided into four modules: (i) A trajectory planner computes a path that passes through the sequence of desired gates; (ii) a perception system that obtains the global pose of the vehicle by using an onboard camera; (iii) a localization system which merges several sensed information to estimate the drone's states; and, (iv) an artificial vector field-based controller in charge of following the plan by using the estimated states. To evaluate the performance of the entire integrated system, we extended the FlightGoggles simulator to use gates similar to those used in the AIRR competition. A computational cost analysis demonstrates the high performance of the proposed perception method running on limited hardware commonly embedded in aerial robots. In addition, we evaluate the localization system by comparing it with ground truth and when there is a disturbance in the location of the gates. Results in a representative race circuit based on the AIRR competition showed the proposed strategy's competing performance, presenting itself as a feasible solution for drone racing systems.

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“…CNNs have been applied to identify waypoints in dynamic indoor environments for drone racing [ 122 ]. Waypoints are identified in local-body frame coordinates to deal with the problem of drift, and are integrated with state of the art path planner and tracker.…”

Section: Eye Level Viewmentioning

confidence: 99%

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

et al. 2020

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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

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Deep Drone Racing: From Simulation to Reality With Domain Randomization

Cited by 193 publications

References 47 publications

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

An integrated solution for an autonomous drone racing in indoor environments

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

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