Applied Sciences

Research

15 pages, 5012 KiB

Open AccessArticle

Characterization of a Rectangular-Cut Kirigami Pattern for Soft Material Tuning

by Benigno Muñoz-Barron, X. Yamile Sandoval-Castro, Eduardo Castillo-Castaneda and Med Amine Laribi

Appl. Sci. 2024, 14(8), 3223; https://doi.org/10.3390/app14083223 - 11 Apr 2024

Viewed by 434

Kirigami is the art of cutting paper to create three-dimensional figures for primarily aesthetic purposes. However, it can also modify the mechanical behavior of the resulting structure. In the literature, kirigami has been applied to modify the material’s structural behavior, such as by [...] Read more.

Kirigami is the art of cutting paper to create three-dimensional figures for primarily aesthetic purposes. However, it can also modify the mechanical behavior of the resulting structure. In the literature, kirigami has been applied to modify the material’s structural behavior, such as by changing its elasticity, rigidity, volume, or any other characteristic. This article examines the behavior of a pattern of rectangular kirigami cuts on a thermoplastic polyurethane soft material structure and its influence on the mechanical parameters of the macrostructure. The results demonstrate that rectangular kirigami patterns significantly affect the stiffness of the test specimens, changing from 1635 N/m to 4020 N/m. In elongation, there is a variation from 176.6% to 218% by simply altering the height of the rectangular cut. This enables the adjustment of the soft material structure’s stiffness based on the geometry of the propagating kirigami cuts. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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17 pages, 4863 KiB

Open AccessArticle

Path Planning Method for Manipulators Based on Improved Twin Delayed Deep Deterministic Policy Gradient and RRT*

by Ronggui Cai and Xiao Li

Appl. Sci. 2024, 14(7), 2765; https://doi.org/10.3390/app14072765 - 26 Mar 2024

Viewed by 435

Abstract

This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted [...] Read more.

This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted independently in path planning, resulting in a tortuous path and making it challenging to find an optimal path. The setting of reward functions in policy learning based on DRL is very complex and has poor universality, making it difficult to complete the task in complex path planning. Aiming at the insufficient exploration of the current deterministic policy gradient DRL algorithm twin delayed deep deterministic policy gradient (TD3), a stochastic policy was combined with TD3, and the performance was verified on the simulation platform. Furthermore, the improved TD3 was integrated with RRT* for performance analysis in two-dimensional (2D) and three-dimensional (3D) path planning environments. Finally, a six-degree-of-freedom manipulator was used to conduct simulation and experimental research on the manipulator. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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16 pages, 4455 KiB

Open AccessArticle

Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix

by Haoyu Lin, Pengkun Quan, Zhuo Liang, Dongbo Wei and Shichun Di

Appl. Sci. 2024, 14(5), 2131; https://doi.org/10.3390/app14052131 - 4 Mar 2024

Viewed by 532

Abstract

In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario, data-driven collision localization methods are considered an [...] Read more.

In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario, data-driven collision localization methods are considered an ideal choice. However, due to the typically high demands on the data scale associated with such methods, they may significantly increase the construction cost of models. To mitigate this issue to some extent, in this paper, we propose a novel approach for robot collision localization based on a sparse modular point matrix (SMPM) in the context of automatic charging for electric vehicles. This method, building upon the use of collision point matrix templates, strategically introduces sparsity to the sub-regions of the templates, aiming to reduce the scale of data collection. Additionally, we delve into the exploration of data-driven models adapted to SMPMs. We design a feature extractor that combines a convolutional neural network (CNN) with an echo state network (ESN) to perform adaptive feature extraction on collision vibration signals. Simultaneously, by incorporating a support vector machine (SVM) as a classifier, the model is capable of accurately estimating the specific region in which the collision occurs. The experimental results demonstrate that the proposed collision localization method maintains a collision localization accuracy of 91.27% and a collision localization RMSE of 1.46 mm, despite a 48.15% reduction in data scale. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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22 pages, 1106 KiB

Open AccessArticle

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

by Longfei Jia, Si Zeng, Lei Feng, Bohan Lv, Zhiyuan Yu and Yuping Huang

Appl. Sci. 2023, 13(24), 13334; https://doi.org/10.3390/app132413334 - 18 Dec 2023

Cited by 1 | Viewed by 699

Abstract

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle [...] Read more.

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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21 pages, 15620 KiB

Open AccessArticle

Analysis of the Slanted-Edge Measurement Method for the Modulation Transfer Function of Remote Sensing Cameras

by Jian Yu, Yu Zhang, Biao Qi, Xiaotian Bai, Wei Wu and Hongxing Liu

Appl. Sci. 2023, 13(24), 13191; https://doi.org/10.3390/app132413191 - 12 Dec 2023

Viewed by 1116

Abstract

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The [...] Read more.

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method’s effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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15 pages, 5329 KiB

Open AccessArticle

Developing a Static Kinematic Model for Continuum Robots Using Dual Quaternions for Efficient Attitude and Trajectory Planning

by Yunfei Li, Qiuhao Wang and Qian Liu

Appl. Sci. 2023, 13(20), 11289; https://doi.org/10.3390/app132011289 - 14 Oct 2023

Viewed by 958

Abstract

Kinematic modeling is essential for planning and controlling continuum robot motion. The traditional Denavit Hartenberg (DH) model involves complex matrix multiplication operations, resulting in computationally intensive inverse solutions and trajectory planning. Solving position and orientation changes in continuum robots using the double quaternion [...] Read more.

Kinematic modeling is essential for planning and controlling continuum robot motion. The traditional Denavit Hartenberg (DH) model involves complex matrix multiplication operations, resulting in computationally intensive inverse solutions and trajectory planning. Solving position and orientation changes in continuum robots using the double quaternion rule can reduce computational complexity. However, existing dual quaternion methods are direct equational transformations of DH rules and do not give a complete modeling process. They usually require more interpretability when applying continuum robot kinematic modeling. This paper uses the dual quaternion method to establish a kinematic model of a continuum robot. It uses a two-section continuum robot model to compare the advantages of dual quaternion and traditional modeling methods. In addition, this paper proposes a five-polynomial interpolation algorithm based on the dual quaternion method for trajectory planning of continuum robots. This method accurately models spatial bending and torsional motions of singularity-free continuum robots. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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18 pages, 3710 KiB

Open AccessArticle

Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset

by Elven Kee, Jun Jie Chong, Zi Jie Choong and Michael Lau

Appl. Sci. 2023, 13(20), 11131; https://doi.org/10.3390/app132011131 - 10 Oct 2023

Cited by 1 | Viewed by 952

Abstract

Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they [...] Read more.

Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they can leverage this technology. Therefore, the aim of this study is to develop a smart and lean pick-and-place solution for custom workpieces, which requires minimal computational resources. In this study, we evaluate the effectiveness of illumination and batch size to improve the Average Precision (AP) and detection score of an EfficientDet-Lite model. The addition of 8% optimized bright Alpha3 images results in an increase of 7.5% in AP and a 6.3% increase in F1-score as compared to the control dataset. Using a training batch size of 4, the AP is significantly improved to 66.8% as compared to a batch size of 16 at 57.4%. The detection scores are improved to 80% with a low variance of 1.65 using a uniform 135-angle lamp and 0 illumination level. The pick-and-place solution is validated using Single-Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) Lite. Our experimental results clearly show that the proposed method has an increase of 5.19% in AP compared to SSD MobileNet V2 FPNLite. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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16 pages, 5513 KiB

Open AccessArticle

Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation

by Liduo Liu, Yongji Long, Guoning Li, Ting Nie, Chengcheng Zhang and Bin He

Appl. Sci. 2023, 13(17), 9746; https://doi.org/10.3390/app13179746 - 29 Aug 2023

Cited by 1 | Viewed by 762

Abstract

Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve this problem and enhance the [...] Read more.

Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve this problem and enhance the inference speed of the tracker, fast and accurate visual tracking with a group convolution and pixel-level correlation based on a Siamese network is proposed. The algorithm incorporates multi-layer feature information on the basis of Siamese networks. We designed a multi-scale feature aggregated channel attention block (MCA) and a global-to-local-information-fused spatial attention block (GSA), which enhance the feature extraction capability of the network. The use of a pixel-level mutual correlation operation in the network to match the search region with the template region refines the bounding box and reduces background interference. Comparing our work with the latest algorithms, the precision and success rates on the UAV123, OTB100, LaSOT, and GOT10K datasets were improved, and our tracker was able to run at 40FPS, with a better performance in complex scenes such as those with occlusion, illumination changes, and fast-motion situations. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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10 pages, 412 KiB

Open AccessArticle

An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background

by Zelong Ma, Qinglei Zhao, Xin Che, Xinda Qi, Wenxian Li and Shuxin Wang

Appl. Sci. 2023, 13(14), 8484; https://doi.org/10.3390/app13148484 - 22 Jul 2023

Viewed by 763

Abstract

For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet [...] Read more.

For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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17 pages, 6016 KiB

Open AccessArticle

A Novel Constant Damping and High Stiffness Control Method for Flexible Space Manipulators Using Luenberger State Observer

by Tao Yang, Fang Xu, Si Zeng, Shoujun Zhao, Yuwang Liu and Yanbo Wang

Appl. Sci. 2023, 13(13), 7954; https://doi.org/10.3390/app13137954 - 7 Jul 2023

Cited by 1 | Viewed by 783

Abstract

This paper presents a novel control strategy for transferring large inertia loads using flexible space manipulators in orbit. The proposed strategy employs a Luenberger state observer and damping-stiffness controller to address issues of large tracking error and vibration. A comprehensive joint dynamics model [...] Read more.

This paper presents a novel control strategy for transferring large inertia loads using flexible space manipulators in orbit. The proposed strategy employs a Luenberger state observer and damping-stiffness controller to address issues of large tracking error and vibration. A comprehensive joint dynamics model is developed to identify the main sources of disturbance, and a Luenberger state observer is designed to estimate unmeasurable transmission deformation. Transmission stiffness and load inertia perturbations are identified based on the estimated results. By adjusting velocity damping and the gain of the forward channel, perturbations are suppressed to maintain optimal system damping and stiffness. Simulation and physical experiments demonstrate the effectiveness of the algorithm, with simulation experiments showing smoother joint output characteristics and minimal vibration under large load inertia changes, and a 97% reduction in internal deformation. Physical experiments demonstrate improved joint dynamic command tracking performance, with an 88% reduction in position tracking error. The algorithm provides a practical and efficient approach for transferring large inertia scientific payloads in space. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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17 pages, 8346 KiB

Open AccessArticle

Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network

by Luyao Gao, Shulin Xiao, Changhong Hu and Yang Yan

Appl. Sci. 2023, 13(12), 7143; https://doi.org/10.3390/app13127143 - 14 Jun 2023

Cited by 1 | Viewed by 1149

Abstract

Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing with relationships due to their limitations of local [...] Read more.

Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing with relationships due to their limitations of local feature extraction. Each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data but usually leads to over-smoothing and ignores local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a fusion network based on the GCN and CNN which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with an added attention mechanism. The graph convolutional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To validate the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas. An overall accuracy of 98.78% was obtained in the Indian Pines dataset, and overall accuracies of 98.99% and 98.69% were obtained in the other two datasets. The results show that the proposed fusion network can obtain richer features and achieve a high classification accuracy. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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12 pages, 5344 KiB

Open AccessArticle

Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

by Shuang Dai, Ke-Fei Song, Yan-Long Wang and Pei-Jie Zhang

Appl. Sci. 2023, 13(8), 4939; https://doi.org/10.3390/app13084939 - 14 Apr 2023

Viewed by 1026

Abstract

A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the [...] Read more.

A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the dynamic model using traditional trajectory prediction, we propose a new method for predicting the pitch axis trajectory of the turntable based on the sun vector and a deep learning CNN-LSTM model. First, the ideal solar position of the pitch axis was calculated using the sun vector. Then, the ideal solar position was combined with the running turntable pitch axis motor speed, current, and solar position error signal as the CNN-LSTM model input data. The model parameters were trained and adjusted through test data simulation using Fengyun-3E satellite orbit data. Finally, the next position of the pitch axis was predicted. The test results showed that in the sun vector and CNN-LSTM model, the RMSE value was 0.623 and the MSE value was 0.388. It was better than the LSTM model or CNN model alone and could accurately predict the pitch axis position. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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12 pages, 2354 KiB

Open AccessCommunication

A Method Based on Blackbody to Estimate Actual Radiation of Measured Cooperative Target Using an Infrared Thermal Imager

by Mingyu Yang, Liang Xu, Xin Tan and Honghai Shen

Appl. Sci. 2023, 13(8), 4832; https://doi.org/10.3390/app13084832 - 12 Apr 2023

Viewed by 992

Abstract

Infrared signature of targets is one important approach for target detection and recognition. When measuring the infrared signature of a target in the atmosphere, it is necessary to take the atmospheric transmittance and atmospheric radiation between the measured target and the observer into [...] Read more.

Infrared signature of targets is one important approach for target detection and recognition. When measuring the infrared signature of a target in the atmosphere, it is necessary to take the atmospheric transmittance and atmospheric radiation between the measured target and the observer into account. In this study, a blackbody-based approach for estimating atmospheric transmittance and atmospheric radiation is proposed to improve accuracy. Radiometric calibration is first carried out in the laboratory for the infrared thermal imager to determine the slope and offset used in the linear regression. With a set of different temperatures, radiance of the blackbody and digital number value of images are calculated. Finally, according to the analytical expressions derived, the atmospheric transmittance and atmospheric radiation are determined, and actual radiance for the cooperative target is calculated. Results demonstrate that the uncertainty of the actual radiance of measured cooperative target calculated via the proposed method is lower than that by MODTRAN, from MODTRAN at 5.7% and 16.7%, from proposed method at 2.56% and 10.2% in two experiments. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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14 pages, 5098 KiB

Open AccessArticle

Bionic Design of a Miniature Jumping Robot

by Xianwei Bai, Deyi Kong, Qiong Wang, Xianhai Yu and Xiaoxuan Xie

Appl. Sci. 2023, 13(7), 4534; https://doi.org/10.3390/app13074534 - 3 Apr 2023

Cited by 1 | Viewed by 1573

Abstract

In response to the problem of low energy storage density in the structure of existing miniature jumping robots, this study designed a parallel single-degree-of-freedom double six-link jumping robot by imitating the physiological structure and jumping mechanism of wax cicadas. The designed six-link mechanism [...] Read more.

In response to the problem of low energy storage density in the structure of existing miniature jumping robots, this study designed a parallel single-degree-of-freedom double six-link jumping robot by imitating the physiological structure and jumping mechanism of wax cicadas. The designed six-link mechanism was first mathematically modeled, and to accommodate the jumping structure of this robot, a six-link mechanism with a smaller cam pushrod stroke was obtained by optimizing the linkage size and position parameters in the model. The dynamics of the robot’s jumping process were then analyzed utilizing the second type of Lagrange equation to determine the joint angles of the robot’s jumping phase. The results were compared with an ADAMS-based jumping simulation to verify the validity of the analysis of the dynamics. The feasibility of the structural design was then validated using ADAMS simulations. Finally, a physical prototype of the jumping robot was produced and tested; the findings revealed that the robot had good jumping performance, was stable in the air, fully discharged 600.2 mJ of energy, and was able to overcome obstacles measuring 220 mm in height and 330 mm in distance. The design of the jumping robot provides a novel approach to improving energy storage density and serves as a foundation for future research on footed jumping robots. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)

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