Linear Velocity-Free Visual Servoing Control for Unmanned Helicopter Landing on a Ship With Visibility Constraint

Huang, Yanting; Zhu, Ming; Zheng, Zewei; Low, Kin Huat

doi:10.1109/tsmc.2021.3062712

Cited by 28 publications

(13 citation statements)

References 42 publications

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“…Such a prediction horizon can be long because of frequent and unscheduled maneuvering. The used sub-series after WT are X, A [5] , D [1] , D [2] , D [3] , D [4] , D [5] for all of the 6 DoFs, i.e., 42 sequences in total in s, and the Haar wavelet in (4) is used. Figure 6 For better learning, all of the 6 used DoFs are standardized to have a mean of 0 and a standard deviation of 1, and the sub-series take the divisions shown in the legend of Fig.…”

Section: A Data and Taskmentioning

confidence: 99%

“…The upper right triangular part is for the "Standard mRNFM" model, and the lower left triangular part is for the "Deeper" model. For each DOF-related block, the 7 sub-blocks are respectively for the sub-series D [1] , X, D [2] , D [3] , D [4] , D [5] , A [5] , left to right or top to bottom, in an order from low-frequency part to high-frequency part.…”

Section: Standard Mrnfm Advanced Trials Ablation Studiesmentioning

confidence: 99%

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Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<div><br></div><div>A major revision is done, compared to the previous version.</div><div><br></div><div>Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.<br></div><div><br></div>

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Section: A Data and Taskmentioning

confidence: 99%

Section: Standard Mrnfm Advanced Trials Ablation Studiesmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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“…Meanwhile, the planning and control performance of robotic manipulators on seaborne platforms are affected seriously by the accuracy of ship motion prediction [3] [4]. For the deployment and landing of air vehicles on board, reliable prediction of ship motions is urgently required to ensure the stability and effectiveness of the shipboard landing control system [5].…”

Section: Introductionmentioning

confidence: 99%

“…Second, ship motions are highly coupled [10] and have chaos characteristics [11]. Given these factors, traditional parameter-fixed models, such as those based on Kalman filters [12] [13], and linear models, such as autoregressive (AR) models [5], cannot easily achieve high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2021

Preprint

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“…A trajectory planning and tracking control approach for mobile robots is developed in [6] that uses virtual-goal-guided rapidly exploring random tree (RRT) for trajectory planning and IBVS with the FOV constraints for tracking. A constrained IBVS approach for helicopter landing on a moving platform is proposed in [7] using a control barrier function methodology to satisfy the FOV constraints. A model predictive controller (MPC) for visual servoing of a mobile robot is developed in [8] which handles visibility constraints for IBVS and velocity limits of the robot.…”

Section: Introductionmentioning

confidence: 99%

Combining Motion Primitives and Image-Based Visual Servo Control

Rotithor

Dani

2020

2020 International Symposium on Flexible Automation

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Combining perception feedback control with learning-based open-loop motion generation for the robot’s end-effector control is an attractive solution for many robotic manufacturing tasks. For instance, while performing a peg-in-the-hole or an insertion task when the hole or the recipient part is not visible in the eye-in-the-hand camera, an open-loop learning-based motion primitive method can be used to generate end-effector path. Once the recipient part is in the field of view (FOV), visual servo control can be used to control the motion of the robot. Inspired by such applications, this paper presents a control scheme that switches between Dynamic Movement Primitives (DMPs) and Image-based Visual Servo (IBVS) control combining end-effector control with perception-based feedback control. A simulation result is performed that switches the controller between DMP and IBVS to verify the performance of the proposed control methodology.

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Linear Velocity-Free Visual Servoing Control for Unmanned Helicopter Landing on a Ship With Visibility Constraint

Cited by 28 publications

References 42 publications

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Combining Motion Primitives and Image-Based Visual Servo Control

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