Tracking and Synchronization with Inversion-Based ILC for a Multi-Actuator-Driven Wafer Inspection Cartridge Transport Robot System

Oh, Dong Jun; Baek, Seung Guk; Nam, Kwangwoo; Koo, Ja Choon

doi:10.3390/electronics10232904

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Ensuring efficiency and quality remains a formidable task. Therefore, it is of great significance to realize high-quality path planning technology for oil sample transportation 19 , 20 . Although there are a large number of literatures for reference, the current oil sampling is mainly realized manually, and the research on the path planning of the robot in the process of oil sample transportation is still relatively weak.…”

Section: Introductionmentioning

confidence: 99%

A route planning for oil sample transportation based on improved A* algorithm

Sang,

Chen,

Chen

et al. 2023

Sci Rep

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The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A* algorithm along with a path smoothing technique has been proposed. Firstly, the Directional Search A* algorithm introduces an angle constraint condition through the evaluation function. By converting sharp turns into obtuse angles, the path turning points become smoother. This approach reduces the occurrence of sharp turns in the path, resulting in improved path smoothness. Secondly, the algorithm enhances the distance function to strengthen the directional guidance during the path search. By optimizing the distance function, the algorithm tends to prefer directions that lead towards the target, which helps reduce the search space and shorten the overall path planning time. Additionally, the algorithm removes redundant nodes along the path, resulting in a more concise path representation. Lastly, the algorithm proposes an improved step size adjustment method to optimize the number of path nodes obtained. By appropriately adjusting the step size, the algorithm further reduces the number of nodes, leading to improved path planning efficiency. By applying these methods together, the Directional Search A* algorithm effectively addresses the limitations of the traditional A* algorithm and produces smoother and more efficient path planning results. Simulation experiments comparing the modified A* algorithm with the traditional A* algorithm were conducted using MATLAB. The experimental results demonstrate that the improved A* algorithm can generate shorter paths, with reduced planning time and smoother trajectories. This indicates that the Directional Search A* algorithm, incorporating the angle constraint condition in the evaluation function and the direction-guided strategy, outperforms the traditional A* algorithm in path planning and provides better solutions to the existing issues.

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Section: Introductionmentioning

confidence: 99%

A route planning for oil sample transportation based on improved A* algorithm

Sang,

Chen,

Chen

et al. 2023

Sci Rep

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“…Section 4 shows the proposed motion profile design considering robot dynamics with iterative learning control (ILC). ILC is a feed-forward control technique that uses previous error information and creates an optimal input profile by repeated learning based on the error between the desired and actual profiles [22,23].…”

Section: Introductionmentioning

confidence: 99%

An Asymmetric Velocity Profile for Minimizing Wafer Slippage and Settling Time of a Wafer Transport Robot

et al. 2023

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This paper presents a control solution for minimizing the takt time of a wafer transfer robot that is widely used in the semiconductor industry. To achieve this goal, this work aims to minimize the transfer time while maximizing the transfer accuracy. The velocity profile is newly designed, taking into consideration parameters such as end effector deformation, changes in friction, vibrations, and required position accuracy. This work focused on the difference between the robot’s acceleration and deceleration phases and their contributions to wafer dynamics, resulting in an asymmetric robot motion profile. Mixed cubic and quintic Bezier curves were adopted, and the optimal profile was obtained through genetic algorithms. Additionally, this work combines its newly developed motion profile with an iterative learning control to ensure the best wafer transportation process time. With the presented method, it is possible to achieve a significant reduction in takt time by minimizing wafer slippage and vibration while maximizing robot motion efficiency. All development processes presented in this paper are verified through both simulation and testing.

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