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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 28405

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Special Issue Editors

Prof. Dr. Yong Liu

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Guest Editor

Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
Interests: deep learning; AI; robotics

Dr. Xingxing Zuo

E-Mail Website
Guest Editor

Department of Informatics Informatics 9, Technical University of Munich, Boltzmannstrasse 3 85748 Garching Germany
Interests: robotics; 3D vision; sensor fusion; SLAM

Special Issue Information

Dear Colleagues,

Recent years have witnessed an explosive growth of data, especially data from various sensors. The explosion of data boosts the rapid development of data-driven technologies, especially deep neural networks. Besides, efficient and effective information processing is always one critical concern in principle-driven methods, which are based on theory, physics, and analytics. When deploying data-driven and principle-driven algorithms on resource-constrained edge devices or embedded systems such as IoTs, robots, self-driving cars, and industrial control equipments, the computation effort allocation in the algorithms and the hardware design of computation devices are the key to achieving real-time performance.

This Special Issue focuses on practical and theoretical technologies in the field of efficient processing of sensor data and their applications on intelligent devices such as industrial robots, unmanned vehicles, and fuel cells equipped with intelligent control and decision strategies. The target audience includes researchers in the broad areas of image processing, deep learning, unmanned systems, robotics, IoT and IIoT, control engineering, fuel cell, ASIC and FPGA.

Prof. Dr. Yong Liu
Dr. Xingxing Zuo
Guest Editors

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Keywords

computer vision and its practical applications
intelligent sensors
machine learning
robotics
model compression
deep learning
efficient computing
efficient AI
efficient hardware
intelligent systems and control theory

Published Papers (11 papers)

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Editorial

Jump to: Research

5 pages, 178 KiB

Open AccessEditorial

Efficient Intelligence with Applications in Embedded Sensing

by Xingxing Zuo and Yong Liu

Sensors 2023, 23(10), 4816; https://doi.org/10.3390/s23104816 - 17 May 2023

Viewed by 1094

Abstract

Despite the fact that computational technology continues to rapidly develop, edge devices and embedded systems are still limited in terms of their computation resources due to such factors as power consumption, physical size constraints, and manufacturing cost [...] Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

Research

Jump to: Editorial

13 pages, 3247 KiB

Open AccessArticle

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

by Tao Huang, Zhe Chen, Wang Gao, Zhenfeng Xue and Yong Liu

Sensors 2023, 23(4), 1845; https://doi.org/10.3390/s23041845 - 7 Feb 2023

Cited by 10 | Viewed by 2359

Abstract

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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19 pages, 12937 KiB

Open AccessArticle

OL-SLAM: A Robust and Versatile System of Object Localization and SLAM

by Chao Chen, Yukai Ma, Jiajun Lv, Xiangrui Zhao, Laijian Li, Yong Liu and Wang Gao

Sensors 2023, 23(2), 801; https://doi.org/10.3390/s23020801 - 10 Jan 2023

Cited by 4 | Viewed by 2617

Abstract

This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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19 pages, 1236 KiB

Open AccessArticle

A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM

by Zetao Xia, Yining Wang, Longhua Ma, Yang Zhu, Yongjie Li, Jili Tao and Guanzhong Tian

Sensors 2023, 23(1), 166; https://doi.org/10.3390/s23010166 - 24 Dec 2022

Cited by 9 | Viewed by 1823

Abstract

Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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16 pages, 11778 KiB

Open AccessArticle

An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

by Guijuan Lin, Keyu Liu, Xuke Xia and Ruopeng Yan

Sensors 2023, 23(1), 97; https://doi.org/10.3390/s23010097 - 22 Dec 2022

Cited by 19 | Viewed by 3440

Abstract

Limited by computing resources of embedded devices, there are problems in the field of fabric defect detection, including small defect size, extremely unbalanced aspect ratio of defect size, and slow detection speed. To address these problems, a sliding window multihead self-attention mechanism is proposed for the detection of small targets, and the Swin Transformer module is introduced to replace the main module in the original YOLOv5 algorithm. First, to reduce the distance between several scales, the weighted bidirectional feature network is employed on embedded devices. In addition, it is helpful to improve the perception of small-target faults by incorporating a detection layer to achieve four-scale detection. At last, to improve the learning of positive sample instances and lower the missed detection rate, the generalized focal loss function is finally implemented on YOLOv5. Experimental results show that the accuracy of the improved algorithm on the fabric dataset reaches 85.6%, and the mAP is increased by 4.2% to 76.5%, which meets the requirements for real-time detection on embedded devices. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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12 pages, 4319 KiB

Open AccessArticle

Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction

by Dongyang Li, Jianyi Yang and Yong Liu

Sensors 2022, 22(23), 9247; https://doi.org/10.3390/s22239247 - 28 Nov 2022

Cited by 4 | Viewed by 1722

Abstract

Vibration signal analysis of the traction machine is an important part of the current rotating machinery state recognition technology, and its feature extraction is the most critical step. In this study, the time-frequency characteristics of the vibration of the traction machine under different elevator running directions, running speeds and load weights are analyzed. The novel demodulation method based on time-frequency analysis and principal component analysis (DPCA) is used to extract the periodic modulated wave signal. In order to compare different influence of background noise and unknown frequency influence, the Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT) methods are used to extract the characteristics of the traction machine vibration signal, respectively. Under different load conditions, it is difficult to observe the obvious differences and similarities of the vibration signals of the traction machine by time-frequency method. However, the DPCA demodulation method provides a guarantee for the reliability and accuracy of the state identification of the traction machine. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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22 pages, 1865 KiB

Open AccessArticle

Efficient Object Detection Based on Masking Semantic Segmentation Region for Lightweight Embedded Processors

by Heuijee Yun and Daejin Park

Sensors 2022, 22(22), 8890; https://doi.org/10.3390/s22228890 - 17 Nov 2022

Cited by 4 | Viewed by 3746

Abstract

Because of the development of image processing using cameras and the subsequent development of artificial intelligence technology, various fields have begun to develop. However, it is difficult to implement an image processing algorithm that requires a lot of calculations on a light board. This paper proposes a method using real-time deep learning object recognition algorithms in lightweight embedded boards. We have developed an algorithm suitable for lightweight embedded boards by appropriately using two deep neural network architectures. The first architecture requires small computational volumes, although it provides low accuracy. The second architecture uses large computational volumes and provides high accuracy. The area is determined using the first architecture, which processes semantic segmentation with relatively little computation. After masking the area using the more accurate deep learning architecture, object detection is implemented with improved accuracy, as the image is filtered by segmentation and the cases that have not been recognized by various variables, such as differentiation from the background, are excluded. OpenCV (Open source Computer Vision) is used to process input images in Python, and images are processed using an efficient neural network (ENet) and You Only Look Once (YOLO). By running this algorithm, the average error can be reduced by approximately 2.4 times, allowing for more accurate object detection. In addition, object recognition can be performed in real time for lightweight embedded boards, as a rate of about 4 FPS (frames per second) is achieved. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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16 pages, 3401 KiB

Open AccessArticle

STV-SC: Segmentation and Temporal Verification Enhanced Scan Context for Place Recognition in Unstructured Environment

by Xiaojie Tian, Peng Yi, Fu Zhang, Jinlong Lei and Yiguang Hong

Sensors 2022, 22(22), 8604; https://doi.org/10.3390/s22228604 - 8 Nov 2022

Cited by 2 | Viewed by 1653

Abstract

Place recognition is an essential part of simultaneous localization and mapping (SLAM). LiDAR-based place recognition relies almost exclusively on geometric information. However, geometric information may become unreliable when faced with environments dominated by unstructured objects. In this paper, we explore the role of segmentation for extracting key structured information. We propose STV-SC, a novel segmentation and temporal verification enhanced place recognition method for unstructured environments. It contains a range image-based 3D point segmentation algorithm and a three-stage process to detect a loop. The three-stage method consists of a two-stage candidate loop search process and a one-stage segmentation and temporal verification (STV) process. Our STV process utilizes the time-continuous feature of SLAM to determine whether there is an occasional mismatch. We quantitatively demonstrate that the STV process can trigger false detections caused by unstructured objects and effectively extract structured objects to avoid outliers. Comparison with state-of-art algorithms on public datasets shows that STV-SC can run online and achieve improved performance in unstructured environments (Under the same precision, the recall rate is 1.4∼16% higher than Scan context). Therefore, our algorithm can effectively avoid the mismatching caused by the original algorithm in unstructured environment and improve the environmental adaptability of mobile agents. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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16 pages, 2426 KiB

Open AccessArticle

Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System

by Jie Ren, Yusu Pan, Pantao Yao, Yicheng Hu, Wang Gao and Zhenfeng Xue

Sensors 2022, 22(21), 8437; https://doi.org/10.3390/s22218437 - 2 Nov 2022

Cited by 3 | Viewed by 2285

Abstract

In this research, we present an intelligent forklift cargo precision transfer system to address the issue of poor pallet docking accuracy and low recognition rate when using current techniques. The technology is primarily used to automatically check if there is any pallet that need to be transported. The intelligent forklift is then sent to the area of the target pallet after being recognized. Images of the pallets are then collected using the forklift’s camera, and a deep learning-based recognition algorithm is used to calculate the precise position of the pallets. Finally, the forklift is controlled by a high-precision control algorithm to insert the pallet in the exact location. This system creatively introduces the small target detection into the pallet target recognition system, which greatly improves the recognition rate of the system. The application of Yolov5 into the pallet positional calculation makes the coverage and recognition accuracy of the algorithm improved. In comparison with the prior approach, this system’s identification rate and accuracy are substantially higher, and it requires fewer sensors and indications to help with deployment. We have collected a significant amount of real data in order to confirm the system’s viability and stability. Among them, the accuracy of pallet docking is evaluated 1000 times, and the inaccuracy is kept to a maximum of 6 mm. The recognition rate of pallet recognition is above 99.5% in 7 days of continuous trials. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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24 pages, 20234 KiB

Open AccessArticle

Pantograph Detection Algorithm with Complex Background and External Disturbances

by Ping Tan, Zhisheng Cui, Wenjian Lv, Xufeng Li, Jin Ding, Chuyuan Huang, Jien Ma and Youtong Fang

Sensors 2022, 22(21), 8425; https://doi.org/10.3390/s22218425 - 2 Nov 2022

Cited by 4 | Viewed by 2307

Abstract

As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene “overlapping” with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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16 pages, 3732 KiB

Open AccessArticle

A Method of Calibration for the Distortion of LiDAR Integrating IMU and Odometer

by Qiuxuan Wu, Qinyuan Meng, Yangyang Tian, Zhongrong Zhou, Cenfeng Luo, Wandeng Mao, Pingliang Zeng, Botao Zhang and Yanbin Luo

Sensors 2022, 22(17), 6716; https://doi.org/10.3390/s22176716 - 5 Sep 2022

Cited by 3 | Viewed by 2553

Abstract

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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