Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 9

Опубликована: Янв. 10, 2022

Mobile robots have an important role in material handling manufacturing and can be used for a variety of automated tasks. The accuracy the robot’s moving trajectory has become key issue affecting its work efficiency. This paper presents method optimizing mobile robot based on digital twin robot. is created by Unity, trained virtual environment applied to physical space. simulation training provides schemes actual movement Based data returned robot, preset dynamically adjusted, which turn enables correction contribution this use genetic algorithms path planning robots, optimization reducing error through interaction real data. It map learning domain

Язык: Английский

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Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection

Frontiers in Neurorobotics, Год журнала: 2022, Номер 16

Опубликована: Май 19, 2022

The development of object detection technology makes it possible for robots to interact with people and the environment, but changeable application scenarios make accuracy small medium objects in practical low. In this paper, based on multi-scale feature fusion indoor target method, using device collect different images angle, light, shade conditions, use image enhancement set up amplify a date set, SSD algorithm layer its adjacent features fusion. Faster R-CNN, YOLOv5, SSD, models were trained an scene data transfer learning. experimental results show that can improve all kinds objects, especially relatively scale. addition, although speed improved decreases, is faster than which better achieves balance between speed.

Язык: Английский

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Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 9

Опубликована: Фев. 11, 2022

With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, is essential to improve robust accuracy of controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller designed based on control. The quantization scaling factors in algorithm are optimized PSO order achieve high robustness manipulator. First all, mathematical model developed, designed. A PD control strategy with compensation for gravity used system. Then, parameters dynamically minute-tuned 1. Through closed-loop loop adjust magnitude factors–proportionality online. Correction values outputted modified 2. factor–proportion factor online achieved find optimal Finally, performance improved verified simulation environment. results show that transient response speed, tracking accuracy, follower characteristics system significantly improved.

Язык: Английский

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Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 10

Опубликована: Апрель 11, 2022

In order to solve the problems of poor image quality, loss detail information and excessive brightness enhancement during in low light environment, we propose a low-light algorithm based on improved multi-scale Retinex Artificial Bee Colony (ABC) optimization this paper. First all, makes two copies original image, afterwards, irradiation component is obtained by used structure extraction from texture via relative total variation for first combines it with obtain reflection which are simultaneously enhanced using histogram equalization, bilateral gamma function correction filtering. next part, second equalization edge-preserving Weighted Guided Image Filtering (WGIF). Finally, weight-optimized fusion performed ABC algorithm. The mean values Information Entropy (IE), Average Gradient (AG) Standard Deviation (SD) images respectively 7.7878, 7.5560 67.0154, improvement compared 2.4916, 5.8599 52.7553. results experiment show that proposed paper improves problem process, enhances sharpness, highlights details, restores color also reduces noise good edge preservation enables better visual perception image.

Язык: Английский

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Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 10

Опубликована: Авг. 16, 2022

The continuous development of deep learning improves target detection technology day by day. current research focuses on improving the accuracy technology, resulting in model being too large. number parameters and speed are very important for practical application embedded systems. This article proposed a real-time method based lightweight convolutional neural network to reduce improve speed. In this article, depthwise separable residual module is constructed combining convolution non-bottleneck-free module, structure used replace VGG backbone SSD feature extraction parameter quantity At same time, kernels 1 × 3 standard adding 1, respectively, obtain multiple graphs corresponding SSD, established integrating information graphs. self-built dataset complex scenes comparative experiments; experimental results verify effectiveness superiority method. tested video performance model, deployed Android platform scalability model.

Язык: Английский

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Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings

Journal of Cultural Heritage, Год журнала: 2024, Номер 68, С. 86 - 98

Опубликована: Май 31, 2024

A prominent feature in Portuguese historic architecture is Portugal's azulejos or tiles that cover cultural heritage buildings with colorful patterns. However, are prone to deterioration due the quality of masonry materials, exposure over time, and natural human factors. careful approach necessary detect assess tile damage time conserve heritage. Deep learning (DL) methods applied by automating vision-based monitoring. This study uses You Only Look Once (YOLO), method automatically. To obtain initial dataset, 5000 images were collected, including cracks, craters, glaze detachment, lacunae, as well no defects. Additionally, a MobileNet model was used for binary classification damaged intact compare detection approaches. Through fine-tuning hyperparameters updating an overall accuracy 72% YOLO (multiple classification) 97% achieved, demonstrating adequacy tool real-world applications.

Язык: Английский

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A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 10

Опубликована: Май 19, 2022

The analysis of robot inverse kinematic solutions is the basis control and path planning, great importance for research. Due to limitations analytical geometric methods, intelligent algorithms are more advantageous because they can obtain approximate directly from robot's positive equations, saving a large number computational steps. Particle Swarm Algorithm (PSO), as one algorithms, widely used due its simple principle excellent performance. In this paper, we propose an improved particle swarm algorithm kinematics solving. Since setting weights affects global local search ability algorithm, paper proposes adaptive weight adjustment strategy improving ability. Considering running time condition based on limit joints, introduces position coefficient k in velocity factor. Meanwhile, exponential product form modeling method (POE) spinor theory chosen. Compared with traditional DH method, approach describes motion rigid body whole avoids singularities that arise when described by coordinate system. order illustrate advantages terms accuracy, time, convergence adaptability, three experiments were conducted general six-degree-of-freedom industrial robotic arm, PUMA560 arm seven-degree-of-freedom research objects. all experiments, parameters range joint angles, initial attitude end-effector given, impact point set verify whether angles found reach specified positions. Experiments 2 3, proposed compared (PSO) quantum (QPSO) direction solving operation convergence. results show other two ensure higher accuracy orientation end-effector. error 0 maximum 1.29 × 10-8. while minimum -1.64 10-5 -4.03 10-6. has shorter algorithms. last computing 0.31851 0.30004s respectively, shortest 0.33359 0.30521s respectively. convergence, achieve faster stable than After changing experimental subjects, still maintains which indicates applicable certain potential multi-arm solution. This provides new way thinking solution problem.

Язык: Английский

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Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 10

Опубликована: Март 22, 2022

Complete trajectory planning includes path planning, inverse solution solving and optimization. In this paper, a highly smooth time-saving approach to is obtained by improving the kinematic optimization algorithms for time-optimal problem. By partitioning joint space, paper obtains an calculation based on of saving 40% kinematics time. This means that large number computational resources can be saved in planning. addition, improved sparrow search algorithm (SSA) proposed complete trajectory. A Tent chaotic mapping was used optimize way generating initial populations. The further combining it with adaptive step factor. experiments demonstrated performance SSA. robot’s optimized time algorithm. Experimental results show method improve convergence speed global capability ensure trajectories.

Язык: Английский

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Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

Frontiers in Bioengineering and Biotechnology, Год журнала: 2022, Номер 9

Опубликована: Янв. 13, 2022

The intelligent monitoring and diagnosis of steel defects plays an important role in improving quality, production efficiency, associated smart manufacturing. application the bio-inspired algorithms to mechanical engineering problems is great significance. split attention network improvement residual network, it visual mechanism bionic algorithm. In this paper, based on feature pyramid improved optimised terms data enhancement, multi-scale fusion structure optimisation. DF-ResNeSt50 model proposed, which introduces a simple modularized block, can improve cross-feature graph groups. Finally, experimental validation proves that proposed has good performance prospects detection defects.

Язык: Английский

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Grasping Pose Detection for Loose Stacked Object Based on Convolutional Neural Network With Multiple Self-Powered Sensors Information

IEEE Sensors Journal, Год журнала: 2022, Номер 23(18), С. 20619 - 20632

Опубликована: Авг. 5, 2022

There are a variety of objects, random postures and multiple objects stacked in disorganized manner unstructured home applications, which leads to the object grasping posture estimation planning based on machine vision become very complicated. This paper proposes method cluttering pose detection convolutional neural network with self-powered sensors information. Firstly, search strategy for candidate poses 3D point cloud is proposed, single-channel image dataset representing this established by using Bigbird dataset. Secondly, ResNet constructed rank filter single channel captured images bit pose. It also compared three mainstream classification networks, Inception V2, VGG-A LetNet, perception analysis function execution developed under ROS. The effective manipulator scene scattered piles realized results position combined information sensors, other networks. In environment experiment show that superior average success rate ResNet, InceptionV2, VGGA LetNet networks 90.67%, 82.67%, 86.67% 87.33% respectively, verifies effectiveness superiority deep learn-based model proposed paper.

Язык: Английский

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