Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

Ren, Xiaolin; Li, Hongwen; Li, Yuanchun

doi:10.1109/access.2020.3042207

Cited by 12 publications

(9 citation statements)

References 37 publications

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“…To assess the performance of the proposed IBVS system, we compare our approach with the IBVS based on KF [19], KFANN [21], FL-KF [20], BELM-SVSF-IBVS [6], and ELM-FL-IBVS [18]. In this section, we describe our MATLAB-based experiments on these systems with a robot based on the EIH model.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…According to the type of visual feedback information [6][7][8][9][10], the visual servo divides into position-based visual servoing (PBVS), image-based visual servoing (IBVS) [11], and hybridbased visual servoing (HBVS). Another classification is based on the location of the camera.…”

Section: Related Workmentioning

confidence: 99%

“…To further verify the superior performance of our proposed method, in this case, we compare the proposed FL-KF-AVOA-ELM method with two recent methods, BELM-SVSF [6] and ELM-FL [18] respectively. We conducted comparative experiments under the same initial conditions (same feature points, desired features, and initial joint angles of the manipulator).…”

Section: Case 2: Comparison With Belm-svsf-ibvs and Elm-fl-ibvsmentioning

confidence: 99%

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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: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Case 2: Comparison With Belm-svsf-ibvs and Elm-fl-ibvsmentioning

confidence: 99%

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

2022

KSII TIIS

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“…Classic visual servoing is a method of controlling the movement of a robot using realtime feedback from vision sensors [7], [8], [9]. Visual servoing control has two basic approaches: Position-based visual servoing and image-based visual servoing [10], [11].…”

Section: Visual Servoing On Rigid Objectsmentioning

confidence: 99%

Visual Servoing on a deformable object with pre-planned trajectory-based geometric primitives

Derrar

Saidi

Abed

2023

Preprint

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This paper presents a novel approach to rigidly align a prespective camera to the deformation of a non-rigid object. We assume having a reference pre-planned camera trajectory viewing a non-rigid object. Our goal is to align this trajec-tory at the execution time given a prior only on the most relevant landmarks. Our method does not assume any parametric or non-parametric prior model on the physics of deformation. The proposed method is formalized as a tracking problem embedded in an optimal visual control framework. The tracking problem encodes visual servoing of geometric features from the deformable object between pre-planned and execution time. The optimal visual control framework is formalized with a weighted least-square criterion that minimizes the distance between the images of the reference features and the features at runtime. The weights are time-dependent smooth functions that encode the relevance of the visible object’s features. We experimentally prove that our method can adapt to multiple pre-planned trajectories and multiple types of deformations (both linear and nonlinear) without any prior knowledge on them. Robustness to noise in the detection of image features is also experimentally demonstrated.

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“…T. Yüksel proposed an intelligent visual servoing method based on ELM with fuzzy logic while considering visibility constraints [28]. Ren et al proposed a bidirectional ELM‐based approach for constrained visual servoing [29]. In [30], Zhong et al presented an online sequential ELM to forecast the result of feature error and the pseudo‐inverse of the image interaction matrix.…”

Section: Introductionmentioning

confidence: 99%

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Li,

Peng,

et al. 2023

Asian Journal of Control

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In this study, a novel image‐based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo‐inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo‐inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo‐inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6‐DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller.

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Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

Cited by 12 publications

References 37 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

Visual Servoing on a deformable object with pre-planned trajectory-based geometric primitives

Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

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