Hemispherical InfraRed (IR) Marker for Reliable Detection for Autonomous Landing on a Moving Ground Vehicle From Various Altitude Angles

Lim, Jeonggeun; Lee, Tae-Yeon; Pyo, Sangjin; Lee, Jehong; Kim, Juho; Lee, Jong-Ho

doi:10.1109/tmech.2021.3066643

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…with ω p = S −1 (R ⊤ p Ṙp ) being the planned angular velocity that can be computed analytically by time-differentiation of (10), given a sufficiently smooth virtual input u a and Carrier trajectory as per Assumption 1 (we assume constant heading direction set-point h d in this work, given that it does not affect the landing procedure).…”

Section: A Hierarchical Control Design For Underactuated Uavsmentioning

confidence: 99%

Vision-Based Air-to-Air Autonomous Landing of Underactuated VTOL UAVs

Roggi,

Gozzini,

Invernizzi

et al. 2024

IEEE/ASME Trans. Mechatron.

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This paper addresses the problem of Air-to-Air Autonomous Landing (AAAL) of a small underactuated Unmanned Aerial Vehicle (UAV) on a larger one (Carrier) in a non-cooperative manner using vision-based state estimation. A Kalman-filter based state estimator reconstructs the state of the Carrier relying on a camera mounted on the small UAV (Follower). Then, a three-layer hierarchical architecture is proposed. A hybrid automaton based on a quasi-time optimal approach is used at the position layer to ensure a safe and fast landing. An adaptive observer is developed at the velocity layer to track robustly the reference velocity coming from the position layer and to compensate for the lack of information about the Carrier acceleration. An attitude planner and a geometric stabilizer are used at the innermost layer. The proposed control architecture outperforms the one developed in a previous work by reducing the time to land up to 59% and increasing the accuracy up to 47%. Experimental tests have been performed to assess the performance of the proposed algorithm in representative scenarios.

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Section: A Hierarchical Control Design For Underactuated Uavsmentioning

confidence: 99%

Vision-Based Air-to-Air Autonomous Landing of Underactuated VTOL UAVs

Roggi,

Gozzini,

Invernizzi

et al. 2024

IEEE/ASME Trans. Mechatron.

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“…The landing pad was detected for autonomous landing by AprilTags and color segmentation, the experimental results showed that the UAV landing was successful underground, and the vehicle speed was less than 3 m/s [ 10 ]. A hemispherical infrared marker was proposed for the UAV autonomous landing on a moving ground vehicle, and autonomous landing experiments were operated to demonstrate the effectiveness from various angles [ 11 ]. An approach based on deep reinforcement learning was designed for the UAV landing on a moving unmanned ground vehicle (UGV), which achieved a high landing success rate and accuracy [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Landing of Quadrotor Unmanned Aerial Vehicles Based on Multi-Level Marker and Linear Active Disturbance Reject Control

Lv,

Fan,

Fang

et al. 2024

Sensors

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Landing on unmanned surface vehicles (USV) autonomously is a critical task for unmanned aerial vehicles (UAV) due to complex environments. To solve this problem, an autonomous landing method is proposed based on a multi-level marker and linear active disturbance rejection control (LADRC) in this study. A specially designed landing board is placed on the USV, and ArUco codes with different scales are employed. Then, the landing marker is captured and processed by a camera mounted below the UAV body. Using the efficient perspective-n-point method, the position and attitude of the UAV are estimated and further fused by the Kalman filter, which improves the estimation accuracy and stability. On this basis, LADRC is used for UAV landing control, in which an extended state observer with adjustable bandwidth is employed to evaluate disturbance and proportional-derivative control is adopted to eliminate control error. The results of simulations and experiments demonstrate the feasibility and effectiveness of the proposed method, which provides an effective solution for the autonomous recovery of unmanned systems.

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“…They simulated the movement of a ship deck with an attitude-programmable plate. Landing on a moving target [15][16][17] is more challenging compared with ship landing in terms of localization, trajectory planning, and control. For fixed-wing UAVs, however, it is challenging to track the ground marker throughout the entire landing process.…”

Section: Related Workmentioning

confidence: 99%

N-Cameras-Enabled Joint Pose Estimation for Auto-Landing Fixed-Wing UAVs

Tang,

Shen,

Xiang

et al. 2023

Drones

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We propose a novel 6D pose estimation approach tailored for auto-landing fixed-wing unmanned aerial vehicles (UAVs). This method facilitates the simultaneous tracking of both position and attitude using a ground-based vision system, regardless of the number of cameras (N-cameras), even in Global Navigation Satellite System-denied environments. Our approach proposes a pipeline consisting of a Convolutional Neural Network (CNN)-based detection of UAV anchors which, in turn, drives the estimation of UAV pose. In order to ensure robust and precise anchor detection, we designed a Block-CNN architecture to mitigate the influence of outliers. Leveraging the information from these anchors, we established an Extended Kalman Filter to continuously update the UAV’s position and attitude. To support our research, we set up both monocular and stereo outdoor ground view systems for data collection and experimentation. Additionally, to expand our training dataset without requiring extra outdoor experiments, we created a parallel system that combines outdoor and simulated setups with identical configurations. We conducted a series of simulated and outdoor experiments. The results show that, compared with the baselines, our method achieves 3.0% anchor detection precision improvement and 19.5% and 12.7% accuracy improvement of position and attitude estimation. Furthermore, these experiments affirm the practicality of our proposed architecture and algorithm, meeting the stringent requirements for accuracy and real-time capability in the context of auto-landing fixed-wing UAVs.

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Hemispherical InfraRed (IR) Marker for Reliable Detection for Autonomous Landing on a Moving Ground Vehicle From Various Altitude Angles

Cited by 6 publications

References 38 publications

Vision-Based Air-to-Air Autonomous Landing of Underactuated VTOL UAVs

Vision-Based Air-to-Air Autonomous Landing of Underactuated VTOL UAVs

Autonomous Landing of Quadrotor Unmanned Aerial Vehicles Based on Multi-Level Marker and Linear Active Disturbance Reject Control

N-Cameras-Enabled Joint Pose Estimation for Auto-Landing Fixed-Wing UAVs

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