Autonomous Quadcopter Landing on a Moving Target

Gautam, Alvika; Singh, Mandeep; Sujit, P. B.; Saripalli, Srikanth

doi:10.3390/s22031116

Cited by 16 publications

(7 citation statements)

References 48 publications

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“…The movement of a ground vehicle is simulated by attaching a landing platform onto a rover. To the best of our knowledge, only few studies [26]- [28] have been able to verify their approach experimentally and we highlight the advantages of our method in a direct comparison (Table 1).…”

Section: Introductionmentioning

confidence: 91%

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

Xuan-Mung

Nguyen

et al. 2022

IEEE Access

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Unmanned aerial vehicles, especially quadcopters, play key roles in many real-world applications and the related quadcopter autonomous control algorithms have attracted a great deal of attention. In this paper, we address the vision-based autonomous landing problem of a quadcopter on a ground moving target. Firstly, we propose a disturbance observer-based control algorithm, consisting of a nonlinear disturbance observer and robust altitude and attitude controllers. This algorithm is based on the quadcopter dynamics model, and its stability is strictly proved using Lyapunov's theory. Secondly, we develop an autonomous landing planner which we test for various landing scenarios to deliver improved reliability and accuracy of the landing mission. These theoretical studies are complemented by a numerical feasibility study, before demonstrating the effectiveness of our approach under actual flight conditions with an experimental quadcopter platform.INDEX TERMS autonomous vehicle, quadcopter, unmanned aerial vehicle, precision landing, moving target, disturbance observer, robust control, mission planing.

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Section: Introductionmentioning

confidence: 91%

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

Xuan-Mung

Nguyen

et al. 2022

IEEE Access

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“…To the knowledge of this work, there are different kinds of fiducial markers, among which ARTag, AprilTag, ArUcO and STag are the most common [ 17 ]. Works developed by Delbene et al [ 18 ] and Gautam et al [ 19 ] use AprilTags to assist the landing, whereas Chang et al [ 20 ] relies on ArUco tags.…”

Section: Related Workmentioning

confidence: 99%

“…Gautam et al [ 19 ] addresses the same problem by proposing a vision-based guidance approach with a log-polynomial closing velocity controller integrated with pure pursuit guidance. In their work, the landing pad detection algorithm uses a combination of colour segmentation and AprilTags to ensure flexibility and detectability from low and high altitudes.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Autonomous Following of a Moving Platform and Landing for an Unmanned Aerial Vehicle

Morales

Castelo

Serra

et al. 2023

Sensors

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Interest in Unmanned Aerial Vehicles (UAVs) has increased due to their versatility and variety of applications, however their battery life limits their applications. Heterogeneous multi-robot systems can offer a solution to this limitation, by allowing an Unmanned Ground Vehicle (UGV) to serve as a recharging station for the aerial one. Moreover, cooperation between aerial and terrestrial robots allows them to overcome other individual limitations, such as communication link coverage or accessibility, and to solve highly complex tasks, e.g., environment exploration, infrastructure inspection or search and rescue. This work proposes a vision-based approach that enables an aerial robot to autonomously detect, follow, and land on a mobile ground platform. For this purpose, ArUcO fiducial markers are used to estimate the relative pose between the UAV and UGV by processing RGB images provided by a monocular camera on board the UAV. The pose estimation is fed to a trajectory planner and four decoupled controllers to generate speed set-points relative to the UAV. Using a cascade loop strategy, these set-points are then sent to the UAV autopilot for inner loop control. The proposed solution has been tested both in simulation, with a digital twin of a solar farm using ROS, Gazebo and Ardupilot Software-in-the-Loop (SiL); and in the real world at IST Lisbon’s outdoor facilities, with a UAV built on the basis of a DJ550 Hexacopter and a modified Jackal ground robot from DJI and Clearpath Robotics, respectively. Pose estimation, trajectory planning and speed set-point are computed on board the UAV, using a Single Board Computer (SBC) running Ubuntu and ROS, without the need for external infrastructure.

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“…Gautam [13] presented a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared with that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad.…”

Section: A Existing Algorithmsmentioning

confidence: 99%

“…We will elaborate on these related technologies in the "Related work" section. In the relevant state-of-the-art literature that we have collected, most of them [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] studied the landing technology of rotorcraft UAVs, and only [1][2] investigated the technology related to the fixed-wing UAV landing. However, Kong [1] stated that his method relies on the ground-based stereo guidance system, and Zhang [2] focused on a method for high-precision altitude estimation.…”

Section: Introductionmentioning

confidence: 99%

High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

Yuan

Wang

2022

IEEE Access

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Most of the current research on autonomous landing of Unmanned Aerial Vehicles (UAVs) focus on rotorcrafts, which can fly horizontally over the landing site and then land vertically, with less landing risk. However, some of the unmanned fixed-wing aircrafts take off and land in the sliding mode of wheel landing gears, and their landing stage is difficult to control and easier to failure. In this paper, the autonomous landing of the fixed wing UAVs based on the visual navigation is studied. We propose a deep learning based Vision Transformer Particle Region-based Convolutional Neural Network (VitP-RCNN). Our VitP-RCNN uses Mobile Vision Transformers (MobileViT) as the backbone to accelerate feature extraction. Regarding the innovation of the present study, it is noticed that candidate boxes are chosen as the particles in our methodology, where the confidence is defined as the particle weight, the spatio-temopral correlation between adjacent image is used in video tracking and the Particle Filter theory is employed into the two-stage detection network to construct the present VitP-RCNN. Based on the spatial-temporal correlation, Our VitP-RCNN is efficient in predicting the particle state in the next frame through the state of the previous frame, so as to realize the continuous tracking of the landing marker. The experimental results show that on the Jetson AGX Xavier drone platform, our speed is up to 51FPS, enabling us to perform the landing marker tracking for the autonomous landing of the fixed-wing drones.INDEX TERMS Unmanned Aerial Vehicle (UAV); drone; autonomous landing; target detection; target tracking; deep learning

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Autonomous Quadcopter Landing on a Moving Target

Cited by 16 publications

References 48 publications

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

Vision-Based Autonomous Following of a Moving Platform and Landing for an Unmanned Aerial Vehicle

High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

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