Impedance-based Control for Soft UAV Landing on a Ground Robot in Heterogeneous Robotic System

Kalinov, Ivan; Petrovsky, Alexander; Agishev, Ruslan; Karpyshev, Pavel; Tsetserukou, Dzmitry

doi:10.1109/icuas51884.2021.9476826

Cited by 17 publications

(3 citation statements)

References 28 publications

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“…of field of view. Another approaches to dealing with the images in night conditions were proposed in [10], [11]. However, the researchers either use the multispectral imaging or the additional IR infrastructure, which leads to a significant increase in the cost of an autonomous platform.…”

Section: B Problem Statementmentioning

confidence: 99%

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Savinykh¹,

Kurenkov²,

Kruzhkov³

et al. 2022

Preprint

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Existing visual SLAM approaches are sensitive to illumination, with their precision drastically falling in dark conditions due to feature extractor limitations. The algorithms currently used to overcome this issue are not able to provide reliable results due to poor performance and noisiness, and the localization quality in dark conditions is still insufficient for practical use. In this paper, we present a novel SLAM method capable of working in low light using Generative Adversarial Network (GAN) preprocessing module to enhance the light conditions on input images, thus improving the localization robustness. The proposed algorithm was evaluated on a custom indoor dataset consisting of 14 sequences with varying illumination levels and ground truth data collected using a motion capture system. According to the experimental results, the reliability of the proposed approach remains high even in extremely low light conditions, providing 25.1% tracking time on darkest sequences, whereas existing approaches achieve tracking only 0.6% of the sequence time.

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Section: B Problem Statementmentioning

confidence: 99%

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

Savinykh¹,

Kurenkov²,

Kruzhkov³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, these methods have to be robust to real scenes with complex structures, such as ambiguous objects and symmetric landmarks. These problems lead to low accuracy of the visual localization methods and limit their usage for robots in real environment, for example, for delivery [1], [2], warehouse inventory [3]- [6] or even disinfection [7], [8].…”

Section: A Motivationmentioning

confidence: 99%

LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process

Kurenkov¹,

Kalinov²,

Tsetserukou³

2022

Preprint

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Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and homogeneous error assumption. We propose a heterogeneous loss function based on concentrated Gaussian distribution with the Lie group to overcome these difficulties. Following our experiment, the proposed method allows us to speed up the training process significantly (from 300 to 10 epochs) with acceptable error values.

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“…Besides, a semi-autonomous mobile self-leveling landing platform was introduced in [20] to launch and land the drone on terrain unsuitable for UAV take-off and landing. In [21], a method for the soft landing of UAVs on a ground robot based on impedance control was presented, which could decrease the impact force of drone landing by 60%. In [22], an impedance controller for a manipulator incorporated a backstepping controller for the UAV s dynamics was designed, which realized reliable landing of the drone.…”

Section: Introductionmentioning

confidence: 99%

A UAV Autonomous Landing System Integrating Locating, Tracking, and Landing in the Wild Environment

Si,

Li,

Wang

et al. 2024

J Intell Robot Syst

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High-reliability landing systems for unmanned aerial vehicles (UAVs) have gained extensive attention for their applicability in complex wild environments. Accurate locating, flexible tracking, and reliable recovery are the main challenges in drone landing. In this paper, a novel UAV autonomous landing system and its control framework are proposed and implemented. It's comprised of an environmental perception system, an unmanned ground vehicle (UGV), and a Stewart platform to locate, track, and recover the drone autonomously. Firstly, a recognition algorithm based on multi-sensor fusion is developed to locate the target in real time with the help of a one-dimensional turntable. Secondly, a dual-stage tracking strategy composed of a UGV and a landing platform is proposed for dynamically tracking the landing drone. In a wide range, the UGV is in charge of fast-tracking through the artificial potential field (APF) path planning and the model predictive control (MPC) tracking algorithms. While the trapezoidal speed planning is employed in platform controller to compensate for the tracking error of the UGV, realizing the precise tracking to the drone in a small range. Furthermore, a recovery algorithm including an attitude compensation controller and an impedance controller is designed for the Stewart platform, ensuring horizontal and compliant landing of the drone. Finally, extensive simulations and experiments are dedicated to verifying the feasibility and reliability of the developed system and framework, indicating that it is a superior case of UAV autonomous landing in wild environments such as grasslands, slopes, and snow.

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Impedance-based Control for Soft UAV Landing on a Ground Robot in Heterogeneous Robotic System

Cited by 17 publications

References 28 publications

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions

LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process

A UAV Autonomous Landing System Integrating Locating, Tracking, and Landing in the Wild Environment

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