Deep learning-based pose prediction for visual servoing of robotic manipulators using image similarity

He, Yaozhen; Gao, Jian; Chen, Yimin

doi:10.1016/j.neucom.2022.03.045

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…These defects can lead to image features being left out of the field-of-view (FOV) of the camera along with target tracking failure. Yaozhen He et al [14] proposed a deep learning-based visual servoing method. They made the original baseline obtain better pose prediction by using a kind of new training strategy.…”

Section: Position-based Visual Servoingmentioning

confidence: 99%

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

2022

KSII TIIS

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Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

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Section: Position-based Visual Servoingmentioning

confidence: 99%

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

2022

KSII TIIS

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Grasping moving objects with incomplete information in a low-cost robot production line using contour matching based on the Hu moments

Nguyen,

Vo Duy

2024

Results in Engineering

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Learning End-to-End Visual Servoing Using an Improved Soft Actor-Critic Approach With Centralized Novelty Measurement

Gao

Chen

et al. 2023

IEEE Trans. Instrum. Meas.

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Deep learning-based pose prediction for visual servoing of robotic manipulators using image similarity

Cited by 4 publications

References 32 publications

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

Grasping moving objects with incomplete information in a low-cost robot production line using contour matching based on the Hu moments

Learning End-to-End Visual Servoing Using an Improved Soft Actor-Critic Approach With Centralized Novelty Measurement

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