On-board processing for autonomous drone racing: An overview

Rojas-Perez, L. Oyuki; Martínez-Carranza, José

doi:10.1016/j.vlsi.2021.04.007

Cited by 20 publications

(10 citation statements)

References 9 publications

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“…Drone racing pushes not only the mechanical and electrical components of drones to their limits, but is also highly demanding in terms of sensor performance. For an in-depth overview of the many different sensor options for drone racing the reader is referred to [46]. The most common sensors aboard autonomous drones are monocular or stereo cameras combined with IMUs (inertial measurement units) thanks to their low cost, low weight, and mechanical robustness.…”

Section: Camera and Imu Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Drone Racing: A Survey

Hanover¹,

Loquercio²,

Bauersfeld³

et al. 2023

Preprint

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Section: Camera and Imu Modelingmentioning

confidence: 99%

“…Further comments are provided by the winner in [165]. Perez et al provides an interesting overview of the types of hardware used for some of the drone racing competitions mentioned so far [46].…”

Section: Competitionsmentioning

confidence: 99%

Autonomous Drone Racing: A Survey

Hanover¹,

Loquercio²,

Bauersfeld³

et al. 2023

Preprint

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“…Rojas-Perez and Martinez-Carranza [48] present an overview of several hardware choices to deal with the Autonomous Drone Racing problem. They discuss the drones, onboard computers, and sensors used by several research groups in the world.…”

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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“…For controlling a quadrotor through high-speed agile maneuvers, it is necessary to consider external aerodynamic effects, e.g., wind, that is applied on the quadrotor in addition to other constraints: dynamics and obstacle constraints. However, several studies related to agile maneuvers (Neunert et al, 2016), (Zhou et al, 2021), (Rojas-Perez and Martinez-Carranza, 2021), (Song and Scaramuzza, 2020), (Hönig et al, 2018) do not consider such effects, which are very difficult to incorporate when modelling system dynamics, except approximating the quadrotor dynamics with simplified motion model (Torrente et al, 2021). Even if those effects are incorporated, the necessary external aerodynamic effects are difficult to obtain due to high-computational demands that leverage real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Kulathunga

Hamed

Devitt

et al. 2022

IEEE Robot. Autom. Lett.

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This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a profile, external aerodynamic effects that are applied on the quadrotor can be neglected. Hence, we used a simplified motion model to represent the motion of the quadrotor when formulating the Nonlinear Model Predictive Control (NMPC)-based local planner. However, the simplified motion model causes residual dynamics between the high-level planner and the low-level controller. The Sparse Gaussian Process Regression-based technique is proposed to reduce these residual dynamics. The proposed technique is compared with Data-Driven MPC. The comparison results yield that an augmented residual dynamics model-based planner helps to reduce the nominal model error by a factor of 2 on average. Further, we compared the proposed complete framework with four other approaches. The proposed approach outperformed the others in terms of tracking the reference trajectory without colliding with obstacles with less flight time without losing computational efficiency.

show abstract

On-board processing for autonomous drone racing: An overview

Cited by 20 publications

References 9 publications

Autonomous Drone Racing: A Survey

Autonomous Drone Racing: A Survey

An integrated solution for an autonomous drone racing in indoor environments

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

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