Implementation of Autonomous Visual Detection, Tracking and Landing for AR.Drone 2.0 Quadcopter

Respall, Victor Massagué; Sellami, Sami; Afanasyev, Ilya

doi:10.1109/dese.2019.00093

Cited by 21 publications

(12 citation statements)

References 20 publications

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“…The Computer Vision (CV) system plays an important role in a majority of outdoor landing systems since real-time target tracking is required. Color-based detection algorithms [15] or visually distinctive tagging [16] have been applied to recognize a moving platform. Several works merges GPS and CV approaches, e.g., Feng et al [17] observed the landing platform by the camera at a close distance and relied on GPS data otherwise.…”

Section: Related Workmentioning

confidence: 99%

SwarmHive: Heterogeneous Swarm of Drones for Robust Autonomous Landing on Moving Robot

Gupta¹,

Baza²,

Dorzhieva³

et al. 2022

Preprint

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The paper focuses on a heterogeneous swarm of drones to achieve a dynamic landing of formation on a moving robot. This challenging task was not yet achieved by scientists.The key technology is that instead of facilitating each agent of the swarm of drones with computer vision that considerably increases the payload and shortens the flight time, we propose to install only one camera on the leader drone. The follower drones receive the commands from the leader UAV and maintain a collision-free trajectory with the artificial potential field.The experimental results revealed a high accuracy of the swarm landing on a static mobile platform (RMSE of 4.48 cm). RMSE of swarm landing on the mobile platform moving with the maximum velocities of 1.0 m/s and 1.5 m/s equals 8.76 cm and 8.98 cm, respectively. The proposed SwarmHive technology will allow the time-saving landing of the swarm for further drone recharging. This will make it possible to achieve self-sustainable operation of a multi-agent robotic system for such scenarios as rescue operations, inspection and maintenance, autonomous warehouse inventory, cargo delivery, and etc.

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Section: Related Workmentioning

confidence: 99%

SwarmHive: Heterogeneous Swarm of Drones for Robust Autonomous Landing on Moving Robot

Gupta¹,

Baza²,

Dorzhieva³

et al. 2022

Preprint

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“…Computer vision has several implementations in robotics. For instance, Respall et al [2] applied it in quadcopters. This project was focused on tracking an unmanned ground vehicle (UGV) platform by getting color information of the image.…”

Section: Related Workmentioning

confidence: 99%

“…They can be programmed using a multi-platform application called Choregraphe or programming languages such as C++ or Python. Furthermore, the robots have a wide variety of sensors, including seven touch sensors, omnidirectional microphones, ultrasonic sensors, and 2D cameras that allow them to interact with their surroundings 2 . NAO robots are now in their 6 th version and have become a staple in education and research alike.…”

Section: Introductionmentioning

confidence: 99%

A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols

Rodríguez-Tirado

Magallán-Ramírez

Martínez-Aguilar

et al. 2020

2020 13th International Conference on Developments in eSystems Engineering (DeSE)

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Maze navigation is a recurring challenge in robotics competitions, where the aim is to design a strategy for one or several entities to traverse the optimal path in a fast and efficient way. To do so, numerous alternatives exist, relying on different sensing systems. Recently, camera-based approaches are becoming increasingly popular to address this scenario due to their reliability and given the possibility of migrating the resulting technologies to other application areas, mostly related to human-robot interaction. The aim of this paper is to present a pipeline methodology towards enabling a robot solving maze autonomously, by means of computer vision and path planning. Afterwards, the robot is capable of communicating the learned experience to a second robot, which then will solve the same challenge considering its own mechanical characteristics which may differ from the first robot. The pipeline is divided into four steps: (1) camera calibration (2) maze mapping (3) path planning and (4) communication. Experimental validation shows the efficiency of each step towards building this pipeline.

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“…This mode of operation needs considerable logistics, making autonomous landing on a moving platform a better choice for many applications. Aiming at this technology, the works [7][8][9][10] achieve the estimation of the relative position and attitude between quadrotor and platform, and navigation using vision, while the control is carried out using a traditional proportional-integral-derivative (PID) algorithm. However, the quadrotor may be disturbed by ground effects and winds during the landing process.…”

Section: Introductionmentioning

confidence: 99%

“…m/s from its initial position[50,7,2] T m. The three-dimensional (3D) landing trajectory of the quadrotor is shown in Figure5, where the black curves belong to the trajectory cluster planned by MPC during the whole process. It can be seen that the landing accuracy gradually improves as the quadrotor approaches the target.…”

mentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

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Implementation of Autonomous Visual Detection, Tracking and Landing for AR.Drone 2.0 Quadcopter

Cited by 21 publications

References 20 publications

SwarmHive: Heterogeneous Swarm of Drones for Robust Autonomous Landing on Moving Robot

SwarmHive: Heterogeneous Swarm of Drones for Robust Autonomous Landing on Moving Robot

A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

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