Hand–Eye Calibration in Visually-Guided Robot Grinding

Li, Wenlong; Xie, He; Zhang, Gang; Yan, Shuicheng; Yin, Zhouping

doi:10.1109/tcyb.2015.2483740

Cited by 74 publications

(22 citation statements)

References 15 publications

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“…1) The camera, the object of interest, and the robot are in the same Cartesian system of coordinates. camera parameters and rotation matrix are used for object localization [40], [41].…”

Section: A Object Searching and Localizationmentioning

confidence: 99%

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Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Li¹,

Jiang²,

Quan³

et al. 2020

IEEE Access

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Automatic assembly of elastic components is difficult because of the potential deformation of parts during the assembly process. Consequently, robots cannot adapt their manipulation to dynamic changes. Designing systems that learn assembly skills can help in alleviating the uncertain factor for industrialgrade assembly robots. This study proposes a skill acquisition method based on multi-modal information description to realize the assembly of systems with elastic components. This multi-modal information includes two-dimensional images, poses, forces/torques, and robot joint parameters. In this method, robots acquire searching, location determination, and pose adjustment skills using these multi-modal information parameters. As a result, robots can reach the assembly target by analyzing two-dimensional images with no position constraint. While acquiring pose adjustment skills, the reward function with depth and assembly steps is used to improve the learning efficiency. The deep deterministic policy gradient (DDPG) algorithm is applied for acquiring skills. Experiments using a KUKA iiwa robot demonstrated the effectiveness and conciseness of our method. Our results indicate that the robot acquired searching, location determination, and pose adjustment skills that allowed it to successfully complete elastic assembly. INDEX TERMS Industrial robots, acquisition of manipulation skills, deep reinforcement learning, multi-modal information description. I. INTRODUCTION A. MOTIVATION Robots are used for more efficient production assembly. Research on automation of assembly systems has focused on intelligent, automatic, and flexible robots. During an actual assembly process, force measurement data are not available owing to elastic deformation between workpieces. Elastic deformation can be neglected when dealing with assembly of rigid parts. However, in the case of weakly rigid components, such deformation causes a series of uncertain changes, such as position, posture, and depth changes. There are methods based on force-control and compliance control that are already facing the problem of assembly of elastic The associate editor coordinating the review of this article and approving it for publication was Okyay Kaynak.

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“…1) The camera, the object of interest, and the robot are in the same Cartesian system of coordinates. camera parameters and rotation matrix are used for object localization [40], [41].…”

Section: A Object Searching and Localizationmentioning

confidence: 99%

“…Results for different discount factors.1) CAMERA CALIBRATIONThe camera interior parameter M using the Zhang's calibration algorithm[40] and the coordinate transformation matrix R using target calibration method[41], we obtain…”

mentioning

confidence: 99%

Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Li¹,

Jiang²,

Quan³

et al. 2020

IEEE Access

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show abstract

“…The effects of the robot pose error on the solution accuracy are not fully considered in their methods which are applied only in the case of low accuracy requirement. Some researchers have proposed to calibrate the hand-eye and the robot simultaneously, 24,25 which leads to complicated derivation and may arise linear correlation problems in parameter estimation. Especially for parallel or hybrid robots, the error modeling is actually a complicated problem.…”

Section: Introductionmentioning

confidence: 99%

Hand-eye calibration method with a three-dimensional-vision sensor considering the rotation parameters of the robot pose

Ding

Huang

et al. 2020

International Journal of Advanced Robotic Systems

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Hand-eye calibration is a fundamental step for a robot equipped with vision systems. However, this problem usually interacts with robot calibration because robot geometric parameters are not very precise. In this article, a new calibration method considering the rotation parameters of the robot pose is proposed. First, a constraint least square model is established assuming that each spherical center measurement of standard ball is equal in the robot base frame, which provides an initial solution. To further improve the solution accuracy, a nonlinear calibration model in the sensor frame is established. Since it can reduce one error accumulation process, a more accurate reference point can be used for optimization. Then, the rotation parameters of the robot pose whose slight errors cause large disturbance to the solution are selected by analyzing the coefficient matrices of the error items. Finally, the hand-eye transformation parameters are refined together with the rotation parameters in the nonlinear optimization solution. Some comparative simulations are performed between the modified least square method, constrained least square method, and the proposed method. The experiments are conducted on a 5-axis hybrid robot named TriMule to demonstrate the superior accuracy of the proposed method.

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“…Automatic grinding robot systems have been widely studied by researchers [2][3][4][5][6]. In practice, actuator non-linearities, including dead zones, backlashes, and saturation, are quite common [7].…”

Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

et al. 2019

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The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.

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Hand–Eye Calibration in Visually-Guided Robot Grinding

Cited by 74 publications

References 15 publications

Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Manipulation Skill Acquisition for Robotic Assembly Based on Multi-Modal Information Description

Hand-eye calibration method with a three-dimensional-vision sensor considering the rotation parameters of the robot pose

A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

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