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Keywords = 3D-VFH

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18 pages, 23703 KB  
Article
Asymmetry Elliptical Likelihood Potential Field for Real-Time Three-Dimensional Collision Avoidance in Industrial Robots
by Ean-Gyu Han, Dong-Min Seo, Jun-Seo Lee, Ho-Young Kim, Shin-Yeob Kang, Ho-Joon Yang and Tae-Koo Kang
Electronics 2025, 14(6), 1102; https://doi.org/10.3390/electronics14061102 - 11 Mar 2025
Viewed by 745
Abstract
Industrial robots play a crucial role in modern manufacturing, but ensuring safe human–robot collaboration remains a challenge. Traditional collision avoidance methods, such as physical barriers and emergency stops, are limited in efficiency and flexibility. This study proposes the Asymmetry Elliptical Likelihood Potential Field [...] Read more.
Industrial robots play a crucial role in modern manufacturing, but ensuring safe human–robot collaboration remains a challenge. Traditional collision avoidance methods, such as physical barriers and emergency stops, are limited in efficiency and flexibility. This study proposes the Asymmetry Elliptical Likelihood Potential Field (AELPF) algorithm, a novel real-time collision avoidance system inspired by autonomous driving technologies. The AELPF method leverages LiDAR sensors to dynamically compute an asymmetric elliptical repulsive field, enabling precise obstacle detection and avoidance in 3D environments. Unlike conventional potential field approaches, the AELPF accounts for both vertical and horizontal deviations, allowing it to adapt to complex industrial settings. To quantify the performance of AELPF, we compare it to two commonly used algorithms: the Vector Field Histogram (VFH) and the Follow the Gap Method (FGM). In terms of processing time, the VFH algorithm requires 50 ms per cycle, while the FGM algorithm operates at 22 ms. In contrast, the the AELPF, when using only a single channel, processes at 12 ms, which is significantly faster than both the VFH and FGM. These results indicate that the AELPF not only provides faster decision-making but also ensures smoother, more responsive navigation in dynamic environments. Both simulation and physical experiments confirm that the AELPF significantly improves obstacle avoidance, particularly in the z-axis direction, reducing the risk of collisions while maintaining operational efficiency. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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15 pages, 4871 KB  
Article
A Time–Frequency Domain Analysis Method for Variable Frequency Hopping Signal
by Zhengzhi Zeng, Chunshan Jiang, Yuanming Zhou and Tianwei Zhou
Sensors 2024, 24(19), 6449; https://doi.org/10.3390/s24196449 - 5 Oct 2024
Cited by 3 | Viewed by 1769
Abstract
A variable frequency hopping (VFH) signal is a kind of frequency hopping (FH) signal that varies both in frequency and dwell time. However, in radio surveillance, the existing methods for unidentified signals using VFH cannot be effectively handled. In this paper, we proposed [...] Read more.
A variable frequency hopping (VFH) signal is a kind of frequency hopping (FH) signal that varies both in frequency and dwell time. However, in radio surveillance, the existing methods for unidentified signals using VFH cannot be effectively handled. In this paper, we proposed an improved joint analysis method based on time–frequency domain features, which adopts multi-level processing to solve the time–frequency domain feature analysis problem of the VFH signal. First, the received signal is pre-processed by Short-Time Fourier Transform (STFT) and binarization, and a highly discriminative time–frequency image is obtained; then, the fixed frequency signal is removed based on the feature of connected domains, and the conventional frequency hopping (CFH) signal is removed by density-based spatial clustering of applications with noise (DBSCAN); finally, the overlapping region is cropped by the joint energy peak time–domain continuity properties. After the above multi-level joint processing method, the problem of VFH signal processing is effectively solved. The simulation result shows that the Mean Square Error (MSE) between the output results and the time–frequency image of the original VFH signal tends to be close to 0 when the Signal-to-Noise ratio (SNR) is 5 dB. Full article
(This article belongs to the Section Electronic Sensors)
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20 pages, 15162 KB  
Article
ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds
by Yaoqiang Pan, Xvlin Xiao, Kewei Hu, Hanwen Kang, Yangwen Jin, Yan Chen and Xiangjun Zou
Agronomy 2024, 14(4), 697; https://doi.org/10.3390/agronomy14040697 - 28 Mar 2024
Cited by 6 | Viewed by 2279
Abstract
In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on [...] Read more.
In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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20 pages, 8360 KB  
Article
Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach
by Xinhua Wang, Mingyan Cheng, Shuai Zhang and Huajun Gong
Drones 2023, 7(8), 504; https://doi.org/10.3390/drones7080504 - 2 Aug 2023
Cited by 14 | Viewed by 3763
Abstract
In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in [...] Read more.
In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight. Full article
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19 pages, 5052 KB  
Article
The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments
by Tiantian Dong, Yonghong Zhang, Qianyu Xiao and Yi Huang
Sensors 2023, 23(13), 5896; https://doi.org/10.3390/s23135896 - 25 Jun 2023
Cited by 4 | Viewed by 2731
Abstract
This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the [...] Read more.
This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance. Full article
(This article belongs to the Section Vehicular Sensing)
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21 pages, 4883 KB  
Article
Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor
by Kamil Sidor and Marian Wysocki
Sensors 2020, 20(10), 2940; https://doi.org/10.3390/s20102940 - 22 May 2020
Cited by 13 | Viewed by 3023
Abstract
In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud [...] Read more.
In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud Library. Recognition is performed by two types of classifiers: (i) k-NN nearest neighbors’ classifier with Dynamic Time Warping measure, (ii) bidirectional long short-term memory (BiLSTM) deep learning networks. Reduction of classification time for the k-NN by introducing a two tier model and improvement of BiLSTM-based classification via transfer learning and combining multiple networks by fuzzy integral are discussed. Our classification results obtained on two representative datasets: University of Texas at Dallas Multimodal Human Action Dataset and Mining Software Repositories Action 3D Dataset are comparable or better than the current state of the art. Full article
(This article belongs to the Section Physical Sensors)
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15 pages, 6147 KB  
Article
An Improved Point Cloud Descriptor for Vision Based Robotic Grasping System
by Fei Wang, Chen Liang, Changlei Ru and Hongtai Cheng
Sensors 2019, 19(10), 2225; https://doi.org/10.3390/s19102225 - 14 May 2019
Cited by 21 | Viewed by 4467
Abstract
In this paper, a novel global point cloud descriptor is proposed for reliable object recognition and pose estimation, which can be effectively applied to robot grasping operation. The viewpoint feature histogram (VFH) is widely used in three-dimensional (3D) object recognition and pose estimation [...] Read more.
In this paper, a novel global point cloud descriptor is proposed for reliable object recognition and pose estimation, which can be effectively applied to robot grasping operation. The viewpoint feature histogram (VFH) is widely used in three-dimensional (3D) object recognition and pose estimation in real scene obtained by depth sensor because of its recognition performance and computational efficiency. However, when the object has a mirrored structure, it is often difficult to distinguish the mirrored poses relative to the viewpoint using VFH. In order to solve this difficulty, this study presents an improved feature descriptor named orthogonal viewpoint feature histogram (OVFH), which contains two components: a surface shape component and an improved viewpoint direction component. The improved viewpoint component is calculated by the orthogonal vector of the viewpoint direction, which is obtained based on the reference frame estimated for the entire point cloud. The evaluation of OVFH using a publicly available data set indicates that it enhances the ability to distinguish between mirrored poses while ensuring object recognition performance. The proposed method uses OVFH to recognize and register objects in the database and obtains precise poses by using the iterative closest point (ICP) algorithm. The experimental results show that the proposed approach can be effectively applied to guide the robot to grasp objects with mirrored poses. Full article
(This article belongs to the Special Issue Visual Sensors)
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22 pages, 13096 KB  
Article
Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor
by Lvwen Huang, Siyuan Chen, Jianfeng Zhang, Bang Cheng and Mingqing Liu
Sensors 2017, 17(9), 1932; https://doi.org/10.3390/s17091932 - 23 Aug 2017
Cited by 24 | Viewed by 8716
Abstract
Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and [...] Read more.
Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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14 pages, 7101 KB  
Article
Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram
by Chin-Sheng Chen, Po-Chun Chen and Chih-Ming Hsu
Sensors 2016, 16(11), 1969; https://doi.org/10.3390/s16111969 - 23 Nov 2016
Cited by 24 | Viewed by 8536
Abstract
This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) [...] Read more.
This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object’s pose and enhances the system’s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems. Full article
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