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检索条件"任意字段=2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023"

共 1298 条记录，以下是111-120 订阅

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Research on biomimetic hexapod unmanned combat platform based on machine vision 4

Research on biomimetic hexapod unmanned combat platform base...

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4th international conference on Mechanical engineering, Intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Yin, Zefeng Li, Zhongxin Xu, Jinxiao School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510674820

In response to the requirements for target identification and tracking in battlefield scenarios, a design has been developed that integrates the control methodologies of hexapod robots with the principles of machine vision. This encompasses the creation of a compact, biomimetic hexapod unmanned combat robot that is based on machine vision principles. The core approach involves the utilization of a high-frame-rate wide-angle camera mounted on the hexapod robot to perform target recognition and tracking on chest ring targets. This is achieved by employing the YOLOv5 and Deepsort algorithms for target detection and tracking, allowing the acquisition of target centroid coordinates. These coordinates are then transmitted to an Arduino development board to govern the hexapod robot's movement. Furthermore, the system has been augmented with an electronic ignition module and firing mechanism, enabling precise firepower upon target locking. By effectively integrating machine vision and hexapod robot motion control technologies, this system provides a comprehensive exposition of the detailed implementation of automated target engagement. © 2024 SPIE · 0277-786X ·

关键词： Biomimetics

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Detection-segmentation convolutional neural network for autonomous vehicle perception 27

Detection-segmentation convolutional neural network for auto...

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27th international conference on Methods and Models in Automation and robotics (MMAR)

作者： Baczmanski, Maciej Synoczek, Robert Wasala, Mateusz Kryjak, Tomasz AGH Univ Krakow Embedded Vis Syst Grp Dept Automat Control & Robot Krakow Poland

ISBN: (纸本)9798350311075

Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP(50) for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://***/vision-agh/MMAR_2023.

关键词： detection-segmentation convolutional neural network autonomous vehicle embedded vision YOLOP Hybrid-Nets MultiTask V3

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A vision-Based Target Localization Method for Robot-Assisted Sonography 28

A Vision-Based Target Localization Method for Robot-Assisted...

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28th international conference on Automation and Computing, ICAC 2023

作者： Tian, Jinyan Si, Weiyong Wang, Ning Yang, Chenguang School of Automation Science and Engineering South China University of Technology Guangzhou China University of the West of England Bristol Robotics Lab BristolBS16 1QY United Kingdom

ISBN: (纸本)9798350335859

This article proposes a vision-based ultrasound scanning localization method that solves the issue of human pose constraints. Specifically, by proposing an improved AlphaPose network, the neck positioning for individuals with different body types is addressed. The interference caused by other moving human subjects in the scene is resolved through an improved DeepSORT tracking algorithm. The impact of environmental noise interference is reduced through depth information processing techniques. Additionally, a robotic platform for neck ultrasound scanning is constructed. This system is capable of automatically identifying and localizing moving human necks, planning trajectories for the ultrasound probe approach, and achieving real-time interaction. © 2023 IEEE.

关键词： Ultrasonic imaging

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2023 4th international conference on Artificial Intelligence, robotics and control, AIRC 2023

2023 4th International Conference on Artificial Intelligence...

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4th international conference on Artificial Intelligence, robotics and control, AIRC 2023

ISBN: (纸本)9798350348248

The proceedings contain 24 papers. The topics discussed include: CaViT: early stage dental caries detection from smartphone-image using vision transformer;utilizing multi-agent deep reinforcement learning for autonomous intersection management systems: a promising approach;instrumentation and in-mold data acquisition system for injection molding process;towards optimization of math pedagogical techniques and behaviors using decision and ensemble trees;semi-automated video archiving using object detection, tracking and ontological annotation;SoMeMax - a novel ai-driven approach to generate artificial social media content that maximizes user engagement;automatic calibration method for the relationship between a robotic arm and a two-dimensional profile sensor;a survey of cell decomposition-based path planning;and novel parallel rings design endoscope modelling and co-simulation for minimally invasive surgery.

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Design And Trial Production Of Flying Flowers Detection And Information Processing Device Basis On Line Scan Camera 3

Design And Trial Production Of Flying Flowers Detection And ...

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3rd international conference on robotics, Automation and Intelligent control, ICRAIC 2023

作者： Wang, Yuefei Yu, Kai Si, Youyin Duan, Kaiwen Jiang, Ziyan Gu, Haibing Yang, Zhanping Shen, Lin Nantong Cellulose Fibers CO LTD Jiangsu China Jiangsu University School of Food and Biological Engineering Jiangsu China Jiangsu University School of Agricultural Engineering Jiangsu China

ISBN: (纸本)9798350360813

To address the inefficiency and error-prone nature of manual weighing methods used to detect the presence of short fibers, commonly known as 'flying flowers,' in the acetate fiber tow used to make cigarette filter rods, a line scan camera-based visual inspection system was developed. This system operates on an open flying flower integrated machine, where the tow is subjected to specified speed ratios, and roll pressures on the roll, resulting in the opening and stretching of the tow. However, due to factors such as fiber wear and breakage, a certain amount of short fibers are produced. The newly designed visual inspection system aims to overcome the limitations of manual weighing methods and provide improved efficiency and accuracy in detecting the flying flower. This paper mainly focuses on presenting the comprehensive design scheme of the system, as well as discussing the structural composition and working principles of each component. The main objectives achieved include the automation of microfiber scene capture after manual sampling on the production line, the implementation of encoding, storage, reading and control of shooting light sources, and the successful development of Feihua intelligent detection system. In addition, through these information processing and development efforts, significant progress has been made in key technologies related to Feihua intelligent detection. © 2023 IEEE.

关键词： Computer vision

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2023 6th international conference on robotics, control and Automation engineering, RCAE 2023

2023 6th International Conference on Robotics, Control and A...

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6th international conference on robotics, control and Automation engineering, RCAE 2023

ISBN: (纸本)9798350315301

The proceedings contain 86 papers. The topics discussed include: lithium battery capacity estimation based on improved convolutional neural network with self-attention mechanism;a component construction progress surveillance method using LiDAR and BIM;real-time SLAM with point-line features using EDLines in unknown aquatic environments;acoustic emission signal characteristics extraction based on improved EMD method for industrial robot body;research on full-envelope flight control of a new type tail-sitter UAV;exploring the effect of pitching and rolling spine angle for a galloping quadruped robot via trajectory optimization;integrating 3DS max with robot technology: a case study on the robot palletizer system;a pseudo-inverse redundancy-based resolution scheme at the acceleration level to control robotic arm motion;and physics-informed neural network approach for numeric modeling of rapid thermal processing system.

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Hierarchical Visual Localization and Measurement Method for Outdoor Large-Scale Environment 9

Hierarchical Visual Localization and Measurement Method for ...

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9th international conference on Mechatronics and robotics engineering (ICMRE)

作者： Xing, Aoran Wang, Yita Li, Xuehai Chen, Suqin Liu, Wei Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China Southern Univ Sci & Technol Taizhou Res Inst Taizhou Peoples R China

ISBN: (纸本)9781665463263

The current large-scene environment recognition technology is mainly used in the field of automatic driving, providing environmental information for self-driving cars. It has relatively high localization and navigation capabilities and can make efficient driving decisions. This paper proposes a set of hierarchical visual measurement methods based on binocular stereo vision system. Deep learning target detection algorithm is deployed to provide a visual solution for automatic loading and unloading of container ports which are typical large-scale environments and require a perceptual measurement system to locate and measure containers. Experimental results are presented to verify the feasibility and performance of our proposed method.

关键词： hierarchical large-scale environment binocular vision container localization

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A Depth Estimation Network Integrated of Foreground Target Enhanced by Optical Flow and Binocular vision 5

A Depth Estimation Network Integrated of Foreground Target E...

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5th international conference on robotics, Intelligent control and Artificial Intelligence, RICAI 2023

作者： Guan, Le Zhang, Tianqi Wang, Xinyang Zhu, Li School of Mechanical Engineering Dalian University Liaoning China

ISBN: (纸本)9798350357950

In order to improve the accuracy of motion information detection in the unmanned driving field, this research proposes a deep learning network - OFSMDE, which combines optical flow, masking and binocular estimation. Optical flow is the two-dimensional projection of a three-dimensional moving object on a plane, and depth is the three-dimensional information obtained from the camera state and scene. These tasks emphasize the target through the semantics information to improve the estimation accuracy. OFSMDE consists of optical flow prediction module, mask module and orbit depth analysis module. OFSMDE introduces semantics segmentation to enhance the consistency between depth and optical flow information while feature extraction is carried out, and further accurately distinguishes foreground and foreground information by masking, then fuses features through context network at cost, and finally obtains high-resolution disparity by regression. OFSMDE has achieved good performance on KITTI 2015 data set. Compared with the reference algorithm PSMNet, the 3-pixel error is only 1.83%, and it has excellent performance in low-contrast environment with strong robustness. © 2023 IEEE.

关键词： Binocular distance measurement component Mask Optical flow Semantic segmentation

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Simulation Study for Robot-based Single Point Incremental Forming

Simulation Study for Robot-based Single Point Incremental Fo...

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2023 international conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Shaker, Walid K. Klimchick, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia University of Lincoln School of Computer Science Lincoln United Kingdom

ISBN: (纸本)9781665475952

The present study investigates the toolpath definition for the numerical simulation of the Single Point Incremental Forming (SPIF). Since the toolpath definition greatly influences the geometrical accuracy of the formed part, substantial research has been taken to properly generate the toolpath. In this paper, two different toolpath strategies were defined: Z-level, and spiral toolpath. A robot program was created using RoboDK to simulate the SPIF process and to test the generated toolpath over a designed workpiece of a truncated cone. The paper also shows how the SPIF numerical simulation was conducted on Abaqus/CAE software. The results demonstrated that the toolpath was properly designed;however, 0.5% relative error was found between the desired and the simulated cone depth due to the SPIF geometric error. To improve the overall accuracy of the SPIF, a combined stiffness-based and closed loop control algorithm is proposed. © 2023 IEEE.

关键词： Numerical models

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Learning Deep Sensorimotor Policies for vision-based Autonomous Drone Racing

Learning Deep Sensorimotor Policies for Vision-based Autonom...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Fu, Jiawei Song, Yunlong Wu, Yan Yu, Fisher Scaramuzza, Davide Univ Zurich Dept Informat Robot & Percept Grp Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Visual Intelligence & Syst Grp Comp Vision Lab Zurich Switzerland

ISBN: (纸本)9781665491907

The development of effective vision-based algorithms has been a significant challenge in achieving autonomous drones, which promise to offer immense potential for many real-world applications. This paper investigates learning deep sensorimotor policies for vision-based drone racing, which is a particularly demanding setting for testing the limits of an algorithm. Our method combines feature representation learning to extract task-relevant feature representations from high-dimensional image inputs with a learning-by-cheating framework to train a deep sensorimotor policy for vision-based drone racing. This approach eliminates the need for globally-consistent state estimation, trajectory planning, and handcrafted control design, allowing the policy to directly infer control commands from raw images, similar to human pilots. We conduct experiments using a realistic simulator and show that our vision-based policy can achieve state-of-the-art racing performance while being robust against unseen visual disturbances. Our study suggests that consistent feature embeddings are essential for achieving robust control performance in the presence of visual disturbances. The key to acquiring consistent feature embeddings is utilizing contrastive learning along with data augmentation.

关键词： Drones

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