Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control

Tzoumanikas, Dimos; Li, Wenbin; Grimm, Marius; Zhang, Ketao; Kovač, Mirko; Leutenegger, Stefan

doi:10.1002/rob.21821

Cited by 29 publications

(25 citation statements)

References 37 publications

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“…The vision algorithm is comprised of adaptive thresholding, fast undistortion, ellipsecross pattern detection, and relative pose estimation, details of which were comprehensively analyzed in [ 22 ]. Besides, the vision systems of other participants are revealed in [ 10 , 23 , 24 ]. Though the MBZIRC competition offered a valuable opportunity to examine the UAVs’ performance when facing challenging real-world conditions, nighttime landing was still a task beyond its consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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“…Several VIO-based state estimation methods used in MBZIRC 2017 are introduced (Bähnemann et al, 2019;Tzoumanikas et al, 2019).…”

Section: State Estimationmentioning

confidence: 99%

Versatile multilinked aerial robot with tilted propellers: Design, modeling, control, and state estimation for autonomous flight and manipulation

Zhao

Anzai

Shi

et al. 2021

Journal of Field Robotics

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A multilinked structure can benefit aerial robots in terms of both maneuvering and manipulation owing to its ability of aerial transformation. A coplanar multilinked model was developed in our previous study. However, the maneuvering and manipulation performances of that model were limited owing to the weak controllability. Therefore, we adopt tilted propellers in this study to enhance controllability.The related design, modeling, and control method are developed to achieve stable hovering and transformation with tilted propellers. Further, state estimation which involves time synchronization between sensors and multilinked kinematics is also presented in this study to enable fully autonomous flight in outdoor environments.The experimental evaluation of the design, modeling, and control method is performed to verify stability during aerial transformation. While, various autonomous outdoor experiments including trajectory following, fast maneuvering for intercepting a target, object grasping for delivery, and blanket manipulation for firefighting are also performed to verify the versatility of the proposed robot platform.To the best of our knowledge, this is the first study of a multilinked aerial robot that can achieve fully autonomous flight and manipulation tasks in an outdoor environment. We also applied our platform in all challenges of the 2020 Mohammed Bin Zayed International Robotics Competition, and we ranked in the third place in Challenge 1 and in the sixth place in Challenge 3 internationally, thereby demonstrating the reliable flight performance in the fields.

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“…In another research presented in [15], the authors used the global positioning system (GPS) navigation to enable a quadcopter to find a moving platform and a vision-based control to approach and land onto it. Many other vision-based approaches coming with different controllers were presented including the studies in [16] with a model predictive controller, [17] with adaptive proportionalintegral-derivative (PID) altitude controllers, [18] with a neural network controller, [19] with a multi-level fuzzy logic controller, and [20] with a backstepping controller. Meanwhile, some other approaches focus more on improving the landing target state estimation [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment

et al. 2020

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Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method. INDEX TERMS Autonomous landing, precision landing, moving target, quadcopter, heaving platform, ship deck, robust control, sliding mode control, disturbance observer.

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Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control

Cited by 29 publications

References 37 publications

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Versatile multilinked aerial robot with tilted propellers: Design, modeling, control, and state estimation for autonomous flight and manipulation

Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment

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