Design of a robot end effector with measurement system for precise pick-and-place of square objects

Li, Fengchun; Jiang, Yao; Li, Tiemin; Feng, Y.-K.; Chen, Shuqing

doi:10.1016/j.promfg.2020.05.035

Cited by 8 publications

(1 citation statement)

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“…obotic arms have been used in industry for several years to automate strenuous, monotonous, and intricate tasks where there is a critical need for speed and precision (Li et al, 2020;Ragaglia et al, 2017). Therefore, robotic arms find wide range of applications including pick and place actions, painting, assembly, part transfer, welding, coating and many more (Iqbal et al, 2012;Wei, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling and Design of State Estimator for a Pick and Place Robotic Arm

Dandago¹,

Mohammed²,

Ubadike³

et al. 2021

FUOYEJET

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A robust model is essential for the design of system components such as controllers, observers state estimators, and simulators. State estimators are becoming increasingly important in modern systems, especially systems with states that may not be measured with sensors. Therefore, it is imperative to analyze the performance of different modelling and state estimator design techniques. In this research work, a parametric model of a pick and place robotic arm was obtained using system identification technique. Pick and place robotic arms have a lot of industrial applications. The parameters of the obtained model were determined using the general second-order characteristics equation and manual tuning. Furthermore, five state estimators were designed based on the developed model. The accuracy of the model, and the performance of the observers were analyzed. The model was found to provide a good representation of the system. Nonetheless, with very small divergence between the model and the real system. The performance of the observers was found to be dependent on their pole locations; the higher the magnitude of the poles, the higher the state estimators’ gain and the better the estimation provided. It was found out that the state estimators with high gains were more susceptible to measurement noise. Keywords— Modelling, pick and place robots, observers, and state estimators.

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Section: Introductionmentioning

confidence: 99%