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Research on Key Technologies of target location based on Intelligent robot 3

Research on Key Technologies of target location based on Int...

引用

3rd international conference on Computer vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Zhong Guo, Caihui Guangzhou City Construction College Guangdong China Citi University Ulaanbaatar Mongolia

ISBN: (纸本)9781665459112

In recent years, the country has proposed the strategic development goal of 'Made in China 2025', and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely on the advantages of high efficiency and strong adaptability to the working environment to promote the development of intelligent manufacturing industry. Machine vision technology makes robots more intelligent, and the robot system integrating vision has also developed rapidly. Compared with monocular vision, binocular vision can obtain target stereo information, and it has become a key research topic in related fields. Under this background, this paper designs an intelligent robot target recognition and positioning system based on binocular vision according to the actual industrial demand. This paper mainly studies the recognition and localization algorithm of circular object workpiece by intelligent robot based on binocular vision system, and designs and realizes the whole experimental system according to the research content. Based on the system experiment, this paper introduces the calibration of the binocular camera system and the robot vision system, the target image pretreatment algorithm, the recognition and positioning algorithm of the target workpiece and so on. Finally, the visual interface is designed to control the robot to complete the simulated grasping action and realize the whole system. The object image is acquired by binocular camera, the object identification and location are completed according to the image processing algorithm, and the robot is controlled to complete the simulation grasping experiment, which has certain reference significance in practical application. © 2022 IEEE.

关键词： Binocular vision

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Hand Gesture Recognition-Based Control System Using Computer vision for Transporter robot

Hand Gesture Recognition-Based Control System Using Computer...

引用

2023 international conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2023

作者： Rianu, Bagas Martinus Kitagawa, Akio Dirgantara, Wahyu Subairi University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Kanazawa University Department of Electrical Engineering and Computer Science Kanazawa Japan

ISBN: (纸本)9798350343892

robotics is a discipline that has experienced rapid development in recent decades. One of the main focuses in robotics development is the advancement of control systems. The primary objective is to create control systems that are more natural, easily understood, easily operated by humans, thereby enabling more intuitive interactions between humans and robots. To achieve this goal, research based on hand gesture recognition has been conducted by adapting computer vision methods as the control system for controlling a transporter robot. In this paper, computer vision methods are employed using the image processing framework called MediaPipe. MediaPipe is used to recognize and comprehend hand gesture movements, as well as display hand landmarks. The research utilizes two hand landmark functions: 'Finger Up' for the right hand to control the wheel movement, and 'Range Gesture' for the left hand to control the arm and gripper of the transporter robot. The testing results indicate that 'Finger Up' function for the right hand successfully controls robot transporter's wheels with a success rate of 100%. Meanwhile, 'Range Gesture' function for the left hand effectively controls the transporter robot's arm and gripper with a success rate of 97%, with a total average error of 3%. © 2023 IEEE.

关键词： Computer control systems

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Object pose estimation for RGB-D based on asymmetric geometric feature extraction fusion 4

Object pose estimation for RGB-D based on asymmetric geometr...

引用

4th international conference on Image processing and Intelligent Control, IPIC 2024

作者： Hu, Shanshan North China University of Technology Beijing China

ISBN: (纸本)9781510682313

6DoF (6 Degrees of Freedom, or 6DoF or 6D) pose estimation has practical applications in robot vision, 3D scene understanding and other fields. 6D pose estimation based on RGB-D images has an important role in robotic applications, however most current methods directly stitch together RGB and depth information, resulting in low accuracy of pose estimation. This paper presents a novel object 6 DoF pose estimation method based on RGB-D images, which improves on the insufficient accuracy of pose estimation. In this paper, we transform the depth map into point clouds, design an asymmetric geometric feature extraction network AGF to effectively extract the geometric information in point clouds, and combine with EfficentNet-B7 network to extract RGB features. In order to make full use of the cross-mode information, the primary and auxiliary feature interaction module MAF is proposed, which can realize the efficient fusion of RGB and point cloud features. Moreover, this paper introduces an adaptive feature contribution weight calculation module AWF, which is able to adaptively calculate the different contribution weights of the two modes to the pose estimation task, thus further improving the estimation accuracy. The experimental results show that the proposed method shows higher accuracy and robustness in the object 6 DoF pose estimation task. © 2024 SPIE.

关键词： robot applications

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Navigational Aid for Open-Ended Surveillance, by Fusing Estimated Depth and Scene Segmentation Maps, Using RGB Images of Indoor Scenes 14

Navigational Aid for Open-Ended Surveillance, by Fusing Esti...

引用

14th IEEE international conference on signal processing and Communications (SPCOM)

作者： Saha, Binoy Shah, Neha Das, Sukhendu IIT Madras Dept CSE Visualizat & Percept Lab Chennai Tamil Nadu India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Open-ended surveillance task for a robot in an unspecified environment using only an RGB camera, has not been addressed at length in literature. This is unlike the popular scenario of path planning where both the target and environments are often known. We focus on the task of a robot which needs to estimate a realistic depiction of the surrounding 3D environment, including the location of obstacles and free space to navigate in the scene within the view field. In this paper, we propose an unsupervised algorithm to iteratively compute an optimal direction for maximal unhindered movement in the scene. This task is challenging when presented with only a single RGB view of the scene, without the use of any online depth sensor. Our process combines cues from two deep-learning processes - semantic segmentation and depth map estimation, to automatically decide plausible robot movement paths while avoiding hindrance posed by objects in the scene. We make assumptions of the use of a low-end RGB USB camera, pre-set camera view direction (angle) and field of view, incremental movement of the robot in the view field, and iterative analysis of the scene, all catering to any open-ended (target-free) surveillance/patrolling applications. Inverse perspective geometry has been used to map the optimal direction estimated in the view field, to that on the floor of the scene for navigation. Results of evaluation using a dataset of videos of scenes captured from indoor (office, labs, meeting/class-rooms, corridors, lounge) environments, reveal the success of the proposed approach.

关键词： robot navigation Freespace estimation Segmentation Depth estimation

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Design and Implementation of Multipurpose robot for Covid-19 Ward 10

Design and Implementation of Multipurpose Robot for Covid-19...

引用

10th international conference on Emerging Trends in Engineering and Technology - signal and Information processing, ICETET-SIP-22 2022

作者： Kale, Chetan Khanapurkar, Milind Kubde, Umesh G H Raisoni College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Nagpur India LGPS Hybrid Energy Pvt Ltd Research and Development Department Maharashtra Nagpur India

ISBN: (纸本)9781665467414

A multipurpose robot is a system for the covid-19 ward to provide support for serving food, medication, perform temperature check etc., for the covid positive patients without making more efforts by the medical or frontline workers towards the care of patients. As everyone there is a high risk getting infected by the virus. So, to avoid visiting rooms by the staff, multipurpose robot can minimize the contacts of patients and frontline workers or medical staff. The robot is controlled by the user using a transmitter section / remote. The main purpose of the robot is to move around, serve the food, check patient vital parameters like temperature, heart rate, SpO2 level wirelessly without making any contact with the patient and can transmit live video of patient using an IP web camera which will be placed on the robot. It will also help to navigate the directions in the hospital so that anyone can easily handle the robot. For the movement of robot from one place to other, authors have used 4 motors with dedicated wheels attached to them, which can be controlled by microcontroller using RF wireless communication module. The robot will be robust enough that it can sustain approximately 5 to 10 kg weight on it. Also, the patients can have a discussion with their families because everyone know family member are not allowed in the covid ward to visit the patient. The transmitter section plays a role of master and the robot plays the role of slave in this system. The multipurpose robot has many advantages with it as it is a low-cost system which requires low maintenance, easy to use, easy to deploy and can-do surveillance as well. © 2022 IEEE.

关键词： COVID-19 Heart rate Wireless communication Temperature sensors Hospitals robot vision systems Maintenance engineering

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IMAGE STEGANALYSIS WITH CONVOLUTIONAL vision TRANSFORMER 47

IMAGE STEGANALYSIS WITH CONVOLUTIONAL VISION TRANSFORMER

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

作者： Luo, Ge Wei, Ping Zhu, Shuwen Zhang, Xinpeng Qian, Zhenxing Li, Sheng Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781665405409

Recent research has shown that deep learning based methods offer more accurate detection for image steganalysis than the traditional detection paradigm based on rich media models. Existing network architectures based on deep learning, however, stack more and more convolutional layers to increase local receptive fields for image stegananlysis. Limited by hardware, the detector with several convolutional layers may not extract features of steganography images from a global perspective effectively. In this paper, we propose a Convolutional vision Transformer for image stegananlysis, which can capture both local and global dependencies among noise features. In image processing phase, our network preserves CNN frame for its capacity of producing image noise residuals. Different from previous methods, we utilize the attention mechanism of vision transformer for feature extraction and classification. The proposed network is validated on two public image datasets (BOSSbase 1.01 and ALASKA #2). Experimental results demonstrate that our network performs well over fixed-size dataset and arbitrary-size dataset.

关键词： Stegananlysis deep leraning convolutional vision transformer

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CONTINUAL LEARNING IN vision TRANSFORMER 29

CONTINUAL LEARNING IN VISION TRANSFORMER

引用

IEEE international conference on Image processing (ICIP)

作者： Takeda, Mana Yanai, Keiji Univ Electrocommun Tokyo Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Continual learning aims to continuously learn new tasks from new data while retaining the knowledge of tasks learned in the past. Recently, the vision Transformer, which utilizes the Transformer initially proposed in natural language processing for computer vision, has shown higher accuracy than Convolutional Neural Networks (CNN) in image recognition tasks. However, there are few methods that have achieved continual learning with vision Transformer. In this paper, we compare and improve continual learning methods that can be applied to both CNN and vision Transformers. In our experiments, we compare several continual learning methods and their combinations to show the differences in accuracy and the number of parameters.

关键词： Continual Learning vision Transformer

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Sample Efficient Reinforcement Learning via Large vision Language Model Distillation

Sample Efficient Reinforcement Learning via Large Vision Lan...

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

作者： Lee, Donghoon Luu, Tung M. Lee, Younghwan Yoo, Chang D. Robotics Program KAIST Daejeon Korea Republic of Electrical Engineering KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent's learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms. The code is available at https://***/i22024/LVLM2P. © 2025 IEEE.

关键词： Knowledge Distillation Reinforcement Learning vision Language Model

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Performance Improvement in Welding Operations Through Image processing 2

Performance Improvement in Welding Operations Through Image ...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sharma, Mohit Kumar Menon, Soumya V Tripathy, Padmaja Vivekananda Global University Department of Electrical Engineering Jaipur India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India ARKA JAIN University Department of Mechnical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

This examination intends to enhance the overall performance of welding operations through picture processing. It's going to use an aggregate of PC vision and gadgets, getting to know to perceive better and tune welds, improve the accuracy of the system, and reduce the capability for mistakes. Particularly, the study will make use of a deep learning method to classify welds in specific classes, allowing the welding operations to be more effectively monitored and operated. Additionally, a convolutional neural network technique will be utilized to pick out the welds and estimate the vital parameters from the image statistics. Sooner or later, a robot arm geared up with a digital camera and a torch can be used to validate the welding process in an actual-world scenario. The effects of this take a look at will be used to enhance the performance and nice of welding operations through higher visibility into the system. © 2024 IEEE.

关键词： Welds

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Research on Interior Design Platform Based on 3D Virtual vision

Research on Interior Design Platform Based on 3D Virtual Vis...

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

作者： Guo, Liming Cui, Donghui Krirk University Thanon Ram Intra Bangkok10220 Thailand

ISBN: (纸本)9781510666351

The existing interior design platform can draw interior design three-dimensional images according to the shape of interior objects. On the basis of existing research, according to the location of indoor objects, generate real and three-dimensional images, and design an indoor design platform based on three-dimensional virtual vision. Determine the structure of the interior design platform by analyzing the three-dimensional perspective, and complete the interior positioning algorithm and interior design demand analysis;By improving the interactive functions of the interior design platform, the anchor points and drivers of the design platform, the interior design platform based on 3D virtual vision is built. The simulation platform uses the environment design contrast experiment, the result shows that the interior design platform based on 3D virtual vision can draw the interior design plane image and three-dimensional image, and the position of the interior objects in the image is more in line with the actual needs. © 2023 SPIE.

关键词： Simulation platform

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