Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach

Chang, Ching Wei; Lo, Li-Yu; Cheung, Hiu Ching; Feng, Yurong; Yang, An-Shik; Wen, Chih-Yung; Zhou, Weifeng

doi:10.3390/s22010404

Cited by 27 publications

(18 citation statements)

References 26 publications

(25 reference statements)

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“…This is one of the examples that highlights the advantage of our approach, where a common log polynomial speed controller function (albeit with different parameter values) for horizontal and vertical speed control provides explicit control over the speed profile characteristics resulting in a time-efficient landing. From an evaluation perspective, most approaches in literature reported the results in the form of aggregate landing accuracy from a limited number of trial runs [ 7 , 27 , 52 ]. We improve on the existing work through a more extensive evaluation.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Quadcopter Landing on a Moving Target

Gautam

Singh

Sujit

et al. 2022

Sensors

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Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of 0.6 m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.

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Section: Discussionmentioning

confidence: 99%

Autonomous Quadcopter Landing on a Moving Target

Gautam

Singh

Sujit

et al. 2022

Sensors

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“…Chang [16] aimed to develop an autonomous system for UAVs to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Their system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system.…”

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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“…Nevertheless, this technology is not always available or it is sometimes incapable of giving an acceptable level of accuracy, as can happen when the inside of a tank of a petrochemical plant is to be inspected. For this reason, in the literature, complementary landing assistance systems (LASs) are proposed based on computer vision techniques [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], a fusion between computer vision techniques and inertial measurement units (IMUs) [ 20 , 21 , 22 , 23 , 24 , 25 ], computer vision, IMU and ultrasonic sensors [ 26 ], computer vision and a Time-of-Flight-based height sensor [ 27 ], computer vision and GNSS [ 28 , 29 ] and even an approach fusing onboard cameras and a robotic total station [ 30 ]. The main setback of traditional vision-based systems is their strong dependency on weather or lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

UWB and IMU-Based UAV’s Assistance System for Autonomous Landing on a Platform

Ochoa-de-Eribe-Landaberea

Zamora-Cadenas

Peñagaricano-Muñoa³

et al. 2022

Sensors

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This work presents a novel landing assistance system (LAS) capable of locating a drone for a safe landing after its inspection mission. The location of the drone is achieved by a fusion of ultra-wideband (UWB), inertial measurement unit (IMU) and magnetometer data. Unlike other typical landing assistance systems, the UWB fixed sensors are placed around a 2 × 2 m landing platform and two tags are attached to the drone. Since this type of set-up is suboptimal for UWB location systems, a new positioning algorithm is proposed for a correct performance. First, an extended Kalman filter (EKF) algorithm is used to calculate the position of each tag, and then both positions are combined for a more accurate and robust localisation. As a result, the obtained positioning errors can be reduced by 50% compared to a typical UWB-based landing assistance system. Moreover, due to the small demand of space, the proposed landing assistance system can be used almost anywhere and is deployed easily.

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Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach

Cited by 27 publications

References 26 publications

Autonomous Quadcopter Landing on a Moving Target

Autonomous Quadcopter Landing on a Moving Target

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

UWB and IMU-Based UAV’s Assistance System for Autonomous Landing on a Platform

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