Intelligent Perception System of Robot Visual Servo for Complex Industrial Environment

Luo, Yongchao; Li, Shipeng; Li, Di

doi:10.3390/s20247121

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…e SI perception system, also known as the perception platform (Server), is composed of four parts: Group Manager (GM), Pseudonym Authority (PA), Data Collectors (DCs), and participants [26]. e specific model is shown in Figure 8.…”

Section: Si Perception System Modelmentioning

confidence: 99%

Copyright Protection and Data Reliability of AI-Written Literary Creations in Smart City

Wei

2022

Security and Communication Networks

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The purpose is to solve copyright disputes over artificial intelligence (AI)-written literary creations and protect copyright by legislation through data reliability research. Accordingly, this work makes a detailed study of the criteria for the copyright protection of AI-written literary creation and the creation process. It constructs the model of swarm intelligence (SI) perception. Then, the Preservation Trustworthiness Incentives Sense (PTISense) scheme is designed based on the encryption algorithm for the SI perception model. The performance of the proposed PTISense scheme is verified and analyzed through experiments. Mainly, it analyzes the impact of PTISense on the accuracy of the reputation model, its robustness against malicious users, and the actual feasibility. The results show that when users complete 50 tasks, the false-positive rate is only 0.1, and the corresponding false-negative rate approximates 0. After each task, the user reputation will be updated, and the data will be evaluated for trust. The trust model of the proposed PTISense scheme based on encryption technology is more accurate. When η (the number of malicious users) is small, the more tasks are performed, the faster the reputation value decreases and tends to zero. The proposed PTISense scheme-based reputation evaluation model can better protect the data submitted by good users. It is robust against malicious users and protects the data and privacy of good users. Further, entities’ computing overhead in different SI perception stages is calculated. It is found that the proposed PTISense scheme is feasible for user data privacy protection. Compared with other schemes, it can achieve a safe and reliable SI perception process with a lower computing overhead. It can better ensure the authenticity and reliability of data.

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Section: Si Perception System Modelmentioning

confidence: 99%

Copyright Protection and Data Reliability of AI-Written Literary Creations in Smart City

Wei

2022

Security and Communication Networks

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“…YOLO-V5 has the characteristics of fast detection speed and high prediction precision and has been studied in industrial [12], agricultural [13] and medicine [9].…”

Section: Discussionmentioning

confidence: 99%

A Novel Method for Reducing Dispensing Error in Hospital Pharmacy

Meng

Wang

2022

Preprint

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Background Dispensing Error occurs frequently in outpatient pharmacies. The aim of this article is to explore the value of the YOLO-V5 deep learning algorithm for object detection in packaging drugs from outpatient pharmacies and to develop an artificial intelligence assist pharmacist drug dispensation system (AI-APDDS) capable of completely reducing or eliminating dispensing errors. Methods A total of 1784 images from 136 different packaging drugs were collected and labeled to form a deep learning dataset. The dataset was split into training and validation sets at a ratio of 3:1. The YOLO-V5 deep-learning algorithm was trained using images from our dataset (training epochs:1000, batch size:4, learning rate:0.01). The values of precision (Pr) and mean average precision(mAP) were used as measures for model performance evaluation. Results Pr in the training set was, for all models, equal to 1.00. The mAP_0.5 of YOLO-V5x was 0.992, which was higher than that of YOLO-V5n (0.975), YOLO-V5s (0.976), YOLO-V5m (0.978), and YOLO-V5l (0.983). The mAP_0.5:0.95 of YOLO-V5x was 0.955, which was higher than those of YOLO-V5n (0.916), YOLO-V5s (0.920), YOLO-V5m (0.921) and YOLO-V5l (0.935). The training time and model size were 92.67 hours and 465MB, respectively, for YOLO-V5x, which were the highest among the four models. The speed of detection for one image was 6.0ms for YOLO-V5n, which was the fastest among the four models. Conclusions It can identify the packaging drugs accurately for five submodels of YOLO-V5, it is feasible to implement the artificial-intelligence assisted drug dispensation system for pharmacist to achieve “zero” dispensing error.

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“…While advanced software and computational capabilities enable robot controllers to produce high-speed, precise instructions, the associated servo system's inertia remains a challenge to the rapid and accurate execution of these directives. Central to this is the servo drive system, pivotal in determining an industrial robot's attributes such as positioning accuracy and operational performance [1]. The permanent magnet synchronous motor (PMSM), with its distinctive characteristics such as flexibility in structure and high efficiency, has garnered significant scholarly interest.…”

Section: Introductionmentioning

confidence: 99%

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

Ji,

Wei,

Wang

et al. 2023

Mathematics

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Industrial robot speed control remains a critical aspect for efficient operations, especially given the challenges of nonlinearity and multivariable characteristics inherent to servo motor control systems, as well as energy inefficiencies due to a lack of automatic speed control. This study refines an existing control algorithm, beetle antennae optimization (BAO), by integrating elements of particle swarm optimization (PSO) and a beetle antennae search algorithm (BAS), further enhanced by chaos mapping and an adaptive weighting factor. These modifications aim to improve the algorithm’s search capabilities and mitigate the risks of settling into local optima. Unlike previous iterations, this study includes rigorous dynamic and stability analyses focusing on key performance metrics such as settling time, overshoot, and steady-state error. Comparative Simulink/MATLAB modeling demonstrates that the enhanced BAO algorithm significantly outperforms traditional PID control, BAS, and adaptive weighted-PSO in reducing static error, overshoot, and adjustment time under various conditions, including scenarios with external disturbances. Our results indicate a 60% improvement in the optimization performance of speed curve metrics, confirming the enhanced efficacy and robustness of the robotic control system. This research offers valuable insights into the advantages of the refined BAO algorithm, providing a comprehensive basis for its practical application in industrial robotic control systems.

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Intelligent Perception System of Robot Visual Servo for Complex Industrial Environment

Cited by 12 publications

References 27 publications

Copyright Protection and Data Reliability of AI-Written Literary Creations in Smart City

Copyright Protection and Data Reliability of AI-Written Literary Creations in Smart City

A Novel Method for Reducing Dispensing Error in Hospital Pharmacy

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

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