Optimization of industrial process parameter control using improved genetic algorithm for industrial robot

Yao, Cenglin; Yong-zhou, LI; Ansari, Mohd Dilshad; Talab, Mohammed Ahmed; Verma, Amit

doi:10.1515/pjbr-2022-0006

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…Docking requires an accurate estimation of the robot's pose, often from a position close to the docking station, through path planning [2]. Mobile robots are used across various fields [3][4][5][6][7][8], including surveillance, planetary exploration, dangerous environments, factory automation, search and rescue operations, and indoor manufacturing environments. The role of mobile robots has become increasingly important for present and future applications.…”

Section: Introductionmentioning

confidence: 99%

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

Jia,

Afaq,

Ripka

et al. 2023

Applied Sciences

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Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.

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Section: Introductionmentioning

confidence: 99%

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

Jia,

Afaq,

Ripka

et al. 2023

Applied Sciences

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Modeling and simulation analysis of optimal layout scheme of aviation logistics park based on genetic algorithm

Chen

2024

Adv Control Appl

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As an important component of modern logistics industry, aviation logistics parks address the problems of unreasonable layout and low efficiency in traditional planning and construction methods. The study first constructed a grid model for optimizing the layout of functional areas in the aviation logistics park, and selected the layout scheme with the highest comprehensive correlation as the optimal planning. At the same time, three important model parameters were determined: prediction of cargo throughput, size of functional areas in the park, and comprehensive correlation values between functional areas. Subsequently, in response to the optimization problem of functional area layout in aviation logistics parks, a cosine adaptive genetic algorithm based on adaptive reversal operation was introduced to solve the model. According to the findings, the research mention algorithm has an average utilization rate of up to 69.64% in the international freight zone, which is a 12.38% improvement over the conventional genetic algorithm. Additionally, it has an average domestic cargo area utilization rate of 67.93%, 11.24% greater than the genetic algorithm. This demonstrated that the functional area layout scheme of the aviation logistics park produced by examining the suggested algorithm is extremely viable and offers new ideas and techniques for the planning and designing of aviation logistics parks.

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An unsupervised chatter detection method based on AE and merging GMM and K-means

Liang

Liu

Yang

et al. 2023

Mechanical Systems and Signal Processing

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Optimization of industrial process parameter control using improved genetic algorithm for industrial robot

Cited by 8 publications

References 31 publications

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

Modeling and simulation analysis of optimal layout scheme of aviation logistics park based on genetic algorithm

An unsupervised chatter detection method based on AE and merging GMM and K-means

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