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Fusion BASED AGV robot Navigation Solution Comparative Analysis and Vrep Simulation 8

Fusion BASED AGV Robot Navigation Solution Comparative Analy...

引用

8th international Iranian conference on signal processing and Intelligent Systems, ICSPIS 2022

作者： Moshayedi, Ata Jahangir Zanjani, S. Mohammadali Xu, Dangling Chen, Xi Wang, Guijiang Yang, Shuxin Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Department of Electrical Engineering Najafabad Branch Najafabad Iran

ISBN: (纸本)9781665476232

Today, Automated Guided Vehicle (AGV) robots are integral to many factories. One of the basic problems of these robots is accurate navigation, which, in addition to creating security in performing tasks, it helps the robot to manage the battery power and energy and move on a predetermined path. Over the various method, the visions are based on good performance, recently been widely used. In this research, a simple but effective fusion method as the combination of vision(camera) and infrared (IR) sensors with the minimum number of sensors is proposed and implemented. The proposed method has been simulated and evaluated using the Vrep simulator with real dimensions of our previous design AGV system named Hongma and the Python API. In the simulation, the proposed method was carried out. In the experiment, five paths named Circle, Elliptical, Spiral,8 shapes, and Special path, different paths with different complexity were tested, and the experiment aimed to find the maximum speed at which the proposed algorithm and the vision sensor (camera sensors) can track the path with a 100% success rate. The results obtained in the experiment show the fusion method's effectiveness over the five mentioned scenarios in tracking the planned path compared to the routine vision method. © 2022 IEEE.

关键词： Computer software

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Real-time vision Based Obstacle Detection in Maritime Environments

Real-time Vision Based Obstacle Detection in Maritime Enviro...

引用

IEEE international conference on Autonomous robot Systems and Competitions (ICARSC)

作者： Nunes, Duarte Fortuna, Joao Damas, Bruno Ventura, Rodrigo CEiiA Ctr Engn & Desenvolvimento Dept Elect & Embedded Syst Ocean & Space Unit Matosinhos Portugal CINAV Ctr Invest Naval Almada Portugal Inst Super Tecn Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9781665482172

Automatic obstacle detection is a key feature for unmanned surface vehicles (USV) operating in a fully autonomous manner. While there are currently many approaches to obstacle detection in maritime environments (e.g., LiDAR, radar) the proposed approach resorts to standard, inexpensive RGB cameras to perform the detection of such obstacles. Recent advances in deep neural network detectors achieve state-of-theart detection results, and some one-stage networks achieve very good results while maintaining inference times small enough to be compatible with real-time capabilities on low-cost embedded processing units. In this paper, we train the YOLO v4 network to detect different types of ships, using publicly available maritime datasets. After training, we evaluate the obtained network on the processing unit located onboard the UAV with respect to detection accuracy and real-time processing capability, thus demonstrating that the presented detection method can be considered a robust, fast, flexible, and inexpensive approach to obstacle detection in USV applications.

关键词： Deep Learning Convolutional Neural Networks Object Detection Embedded Systems Unmanned Surface Vehicles

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Research on Warehouse Object Detection for Mobile robot Based on YOLOv5 2

Research on Warehouse Object Detection for Mobile Robot Base...

引用

2nd international conference on Image processing, Computer vision and Machine Learning, ICICML 2023

作者： Wang, Fei Li, Xingxin Guiyang Institute of Humanities and Technology Faculty of Bigdata and Information Engineering Guiyang China

ISBN: (纸本)9798350331417

In view of the lack of public datasets for object detection in warehouse environment, a large number of images containing cargos, trays and forklifts in real warehouse environment is collected and annotated by cameras, creating a warehouse object dataset. At the same time, in view of the low detection accuracy of traditional object detection algorithms in warehouse environment, the YOLOv5 series models are applied to warehouse environment, and the models are trained and optimized on the warehouse object dataset created by ourselves, achieving accurate detection of warehouse object. The mAP@50 of the YOLOv5 series models has reached more than 99%. Among them, the mAP@50-90 of the YOLOv5l model and YOLOv5x model is about 92%, which is better than other models. The YOLOv5m model is also deployed on the self-designed mobile robot, realizing the automatic detection of warehouse object. The experimental results show that YOLOv5m has achieved a satisfactory warehouse object detection effect, and it has certain practical value in the field of warehouse object detection. © 2023 IEEE.

关键词： Mobile robot Warehouse Object Detection YOLOv5

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Impact of EMG signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw signal 3rd

Impact of EMG Signal Filters on Machine Learning Model Train...

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Barbosa, Ana Ferreira, Edilson Grilo, Vinicius Mattos, Laercio Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Ctr Fed Educ Tecnol Minas Gerais CEFET MG Belo Horizonte MG Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

Our current society faces challenges in integrating individuals with disabilities, making this process difficult and painful. People with disabilities (PwD) are often mistakenly considered incapable due to the difficulties they face in daily tasks due to the lack of adapted means and tools. In this context, assistive technologies play a crucial role in improving the quality of life for these individuals. However, assistive technologies still have various limitations, making research in this area essential to enhance existing solutions and develop new approaches that meet individual needs, aiming to promote inclusion and equal opportunities. This paper presents a research project that focuses on the study of electromyography (EMG) signal processing generated by individuals who have undergone amputations. These signals are essential in assistive technologies, such as myoelectric prostheses. The study focuses on the impact of different filters and machine learning training methods on this processing. The results of this study have the potential to provide relevant findings for the development of more efficient assistive technologies. By understanding the processing of EMG signals and applying machine learning techniques, it is possible to improve the accuracy and response speed of prosthetics, increasing the functionality and naturalness of movements performed by users, as well as paving the way for the emergence of new technologies.

关键词： Assistive technologies Electromyography (EMG) signal processing Machine learning

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Detection Method of Egg Shell Crack Based on Image processing

Detection Method of Egg Shell Crack Based on Image Processin...

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2023 international conference on Image, signal processing, and Pattern Recognition, ISPP 2023

作者： Li, Fei Zhang, Ning College of Mechanical Engineering Xijing University Shaanxi Province Xi'an City China

ISBN: (纸本)9781510666351

As eggs are a very important food source in our daily diet, the inspection of egg quality must be strict. In this paper, a detection method based on machine vision image processing is proposed for the detection of eggshell cracks. Through image pre-processing, the images of the three BGR channels are compared, and then the median filtering is used to derive the image information of the R channel that is more favorable for detection. The image is enhanced to find the ROI, and connected region and to perform image segmentation to get the crack information. Finally, a morphological operation is used to extract the feature value of crack length from the image and conclude that the crack in the eggshell is detected by judging the feature value of the image. The experiments show that the accuracy of the OpenCV image processing-based detection method can reach more than 90%, which indicates that the method of detecting eggshells through image processing has unlimited potential. © 2023 SPIE.

关键词： Image enhancement

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Leveraging Registers in vision Transformers for Robust Adaptation

Leveraging Registers in Vision Transformers for Robust Adapt...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yellapragada, Srikar Thopalli, Kowshik Narayanaswamy, Vivek Sakla, Wesam Liu, Yang Mubarka, Yamen Samaras, Dimitris Thiagarajan, Jayaraman J. Stony Brook University United States Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798350368741

vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of"registers" which are additional tokens that isolate high norm patch tokens while capturing global image-level information has been proposed. While registers have been studied extensively for object discovery, their generalization properties particularly in out-of-distribution (OOD) scenarios, remains underexplored. In this paper, we examine the utility of register token embeddings in providing additional features for improving generalization and anomaly rejection. To that end, we propose a simple method that combines the special CLS token embedding commonly employed in ViTs with the average-pooled register embeddings to create feature representations which are subsequently used for training a downstream classifier. We find that this enhances OOD generalization and anomaly rejection, while maintaining in-distribution (ID) performance. Extensive experiments across multiple ViT backbones trained with and without registers reveal consistent improvements of 2-4% in top-1 OOD accuracy and a 2-3% reduction in false positive rates for anomaly detection. Importantly, these gains are achieved without additional computational overhead. © 2025 IEEE.

关键词： Anomaly Rejection OOD Generalization Registers Robustness vision Transformer

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Anatomical Region Recognition and Real-time Bone Tracking Methods by Dynamically Decoding A-Mode Ultrasound signals 10

Anatomical Region Recognition and Real-time Bone Tracking Me...

引用

10th IEEE RAS/EMBS international conference for Biomedical robotics and Biomechatronics (BioRob)

作者： Lan, Bangyu Stramigioli, Stefano Niu, Kenan Univ Twente Robot & Mechatron Enschede Netherlands

ISBN: (纸本)9798350386523;9798350386530

Accurate bone tracking is crucial for kinematic analysis in orthopedic surgery and prosthetic robotics. Traditional methods (e.g., skin markers) are subject to soft tissue artifacts, and the bone pins used in surgery introduce the risk of additional trauma and infection. For electromyography (EMG), its inability to directly measure joint angles requires complex algorithms for kinematic estimation. To address these issues, A-mode ultrasound-based tracking has been proposed as a non-invasive and safe alternative. However, this approach suffers from limited accuracy in peak detection when processing received ultrasound signals. To build a precise and real-time bone tracking approach, in this paper, a deep learning-based method was introduced for anatomical region recognition and bone tracking using A-mode ultrasound signals, specifically focused on the knee joint. The algorithm is capable of simultaneously performing bone tracking and identifying the anatomical regions where the A-mode ultrasound transducer was placed. It contains the fully connection between all encoding and decoding layers of the cascaded U-Nets to focus only on the signal region that is most likely to have the bone peak, thus pinpointing the exact location of the peak and classifying the anatomical region of the signal. The experiment showed a 97% accuracy in the classification of anatomical regions and a precision of around 0.5 +/- 1mm for tracking movements of the various anatomical areas surrounding the knee joint. In general, this approach shows great potential beyond the traditional method, in terms of the accuracy achieved and the recognition of the anatomical region where the ultrasound has been attached as an additional functionality.

关键词： Ultrasonic transducers

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Virtual Dashboard Design for Grasping Operations in Teleoperation Systems 3

Virtual Dashboard Design for Grasping Operations in Teleoper...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Di Tecco, Antonio Camardella, Cristian Leonardis, Daniele Loconsole, Claudio Frisoli, Antonio Sant'Anna School of Advanced Studies Pisa56127 Italy Universitas Mercatorum Faculty of Technological and Innovation Sciences Rome00186 Italy

ISBN: (纸本)9798350378009

A known issue in teleoperation concerns dexterity in grasping objects, which can be severely limited if not properly coupled with perfect stereoscopic vision and force/tactile feedback. This work presents the development and testing of a subsystem used for a virtual dashboard within the Sully teleoperation system, with hardware and software components. It is used to capture images, gather object information, and display a virtual dashboard that provides the operator with information about the distance and reachability of objects, to support stereoscopic vision visualized through a virtual reality headset. The hardware consists of two front cameras, used as eyes for the teleoperated robot, and two Time-of-Flight (ToF) sensors to estimate the distance between the teleoperated robot and the Object of Interest (OoI). The software processes images captured by cameras to identify the objects, detects the OoI, and estimates its distance from the robot using the measurements of ToF sensors. Performance results showed an optimal trade-off between image quality, frame rate, and use experience. Indeed, a mean accuracy of 88% was achieved in identifying and detecting OoIs, and an average mean absolute percentage error (MAPE) of 1.40% to estimate the distance from the robot to the OoI at 120 cm. © 2024 IEEE.

关键词： Stereo image processing

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A SELF-SUPERVISED PRE-TRAINING FRAMEWORK FOR vision-BASED SEIZURE CLASSIFICATION 47

A SELF-SUPERVISED PRE-TRAINING FRAMEWORK FOR VISION-BASED SE...

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47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hou, Jen-Cheng McGonigal, Aileen Bartolomei, Fabrice Thonnat, Monique Univ Cote Azur INRIA Nice France Aix Marseille Univ Marseille France

ISBN: (纸本)9781665405409

Seizure events feature temporary abnormalities in muscle control or movements. They are usually caused by excessive neuronal activities in the brain, and are called epileptic seizures (ES). Nevertheless, not all seizures are epileptic in origin. Some are caused by psychological reasons, and such type of seizures are called psychogenic non-epileptic seizures (PNES). We propose a method to classify ES and PNES based on clinical signs in the seizure videos. In particular, inspired by BERT, we propose a Transformer-based framework that pre-trains on large unlabeled clinical videos, and then we fine-tune the pre-trained model for seizure classification with a minimum modification. We conduct a leave-one-subject-out (LOSO) validation on our dataset. The F1-score and accuracy are 0.82 and 0.75, respectively. To our knowledge, the proposed approach is the first attempt to use large unannotated data and learn useful representations for downstream tasks in the field of video based seizure analysis.

关键词： Seizure Video Analysis Self-Supervised Learning Computer vision Deep Learning

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Motion Control Study of a Two Wheel-Leg robot with Terrain Adaptation Capability

Motion Control Study of a Two Wheel-Leg Robot with Terrain A...

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international conference on vision, Image and signal processing (ICVISP)

作者： Checao Yu Xuejun Zhu Xinzhi Ma Kun Mao Huige Lai Aidi Yang College of Mechanical Engineering Ningxia university Yinchuan China Science and Technology Research and Development Institute Anhui Feidi Aviation Technology Co. Hefei China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

As a typical floating base robot, the two wheel-leg robot faces challenges such as instability, nonlinearity, and underactuation, making it prone to tip-ping over and instability when moving on complex terrains. To enhance the stability of the two wheel-leg robot when traversing complex terrains, this paper completes the full-body dynamic modeling of the robot through a dis-tributed modeling approach, preserving its dynamic characteristics. The active driving force at the end of the wheel-legs is used to eliminate linearly related passive driving forces, establishing a complete mapping relationship between the resultant force on the torso and the driving force of the wheel-legs. A virtual actuation force distribution constraint based on the attitude angle constraint of the wheel-legs is constructed, and a layered controller for uneven terrain is built. To further improve the terrain adaptability of two wheel-leg robot, a wheel-leg torque solver with the end output force as the control target is constructed based on the distributed full-body dynamic model, achieving a complete mapping between the end output force and the joint torque. An adaptive planning method for the longitudinal trajectory of the torso during jumping motion is proposed, and a full-body torque jumping controller for two wheel-leg robot is established to achieve the jumping motion. Finally, multiple simulation experiments and prototype tests verify the effectiveness of the proposed method.

关键词： Torso Torque Force Dynamics Prototypes signal processing Stability analysis Trajectory Mobile robots Motion control

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