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共 18150 条记录，以下是701-710 订阅

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Bootstrapping robotic Skill Learning with Intuitive Teleoperation: Initial Feasibility Study 18th

Bootstrapping Robotic Skill Learning with Intuitive Teleoper...

引用

18th international conference on Experimental robotics (ISER)

作者： Chu, Xiangyu Tang, Yunxi Kwok, Lam Him Cai, Yuanpei Au, Kwok Wai Samuel Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China Multiscale Med Robot Ctr Sha Tin Hong Kong Peoples R China Tech Univ Munich Munich Germany Cornerstone Robot Ltd Sha Tin Hong Kong Peoples R China

ISBN: (纸本)9783031635984;9783031635960;9783031635953

robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to generate high-quality human demonstrations efficiently for robotic skill learning in a data-driven manner. By using a reliable teleoperation interface, the da Vinci Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration (dS4D) is proposed in this paper. Various manipulation tasks show the system's effectiveness and advantages in efficiency compared to other interfaces. Using the collected data for policy learning has been investigated, which verifies the initial feasibility. We believe the proposed paradigm can facilitate robot learning driven by high-quality demonstrations and efficiency while generating them.

关键词： robot skill learning teleoperation learning from demonstration

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Robust Safety Critical Control for Nonholonomic Wheeled Mobile robot Trajectory Tracking with Matched Disturbance

Robust Safety Critical Control for Nonholonomic Wheeled Mobi...

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

作者： Yulu Ma Lei Liu Changqi Zhu Yanjun Liu Qiang Zeng Liaoning University of Technology College of Electrical Engineering Jinzhou China Liaoning University of Technology College of Science Jinzhou China Dalian Maritime University Navigation College Dalian China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Safety and stability are the key factors for the robot to successfully reach the specified target position, especially in the disturbed environment. In such conditions, the robot is subject to a variety of unknown disturbance, which can lead to system failure. Based on the kinematic model of wheeled mobile robot (WMR), this paper discusses the safety control strategies for trajectory tracking. The proposed strategy combines backstepping control method and control barrier function (CBF), incorporating a disturbance observer to estimate and compensate for disturbances. Then, construct the CBF such that it satisfies the conditions of the safety set and possesses forward invariance. Safety constraint is transformed into the state-dependent linear inequality constraint, and solve the controller using quadratic programming (QP), to ensure collision-free operation of the vehicle in the face of disturbance errors.

关键词： Robust control Backstepping Trajectory tracking Kinematics signal processing Stability analysis Safety Mobile robots Quadratic programming Motion control

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Intelligent Equipment and Special robots - Proceedings of the 2nd international conference, ICIESR 2023

Intelligent Equipment and Special Robots - Proceedings of th...

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2nd international conference on Intelligent Equipment and Special robots, ICIESR 2023

ISBN: (纸本)9781643685144

The proceedings contain 80 papers. The topics discussed include: minibus booming noise reduction based on the driveline system torsional vibration control;design and implementation of multi-function logistics robots for intelligent warehousing;research on stability of integral type second order variable structure control for aircraft pitch channel;design of the slave hand structure and interference detection for the master-slave craniotomy surgical robot;research on speed sensorless vector control of permanent magnet synchronous motor;an intelligent lotus root harvesting equipment;a machine vision-based edge detection method for belt lap of pipe belt conveyor;design of infrared automatic running water alarm device;belt rotation in the pipe conveyor: research on a detection method based on image processing;and design and implementation of post-disaster search and rescue robot based on Beidou navigation and positioning system.

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CONFIDENCE-BASED FEDERATED DISTILLATION FOR vision-BASED LANE-CENTERING

CONFIDENCE-BASED FEDERATED DISTILLATION FOR VISION-BASED LAN...

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

作者： Chen, Yitao Chen, Dawei Wang, Haoxin Han, Kyungtae Zhao, Ming Arizona State Univ Tempe AZ 85287 USA Toyota InfoTech Labs Toyota Japan Georgia State Univ Atlanta GA USA

ISBN: (纸本)9798350302615

A fundamental challenge of autonomous driving is maintaining the vehicle in the center of the lane by adjusting the steering angle. Recent advances leverage deep neural networks to predict steering decisions directly from images captured by the car cameras. Machine learning-based steering angle prediction needs to consider the vehicle's limitation in uploading large amounts of potentially private data for model training. Federated learning can address these constraints by enabling multiple vehicles to collaboratively train a global model without sharing their private data, but it is difficult to achieve good accuracy as the data distribution is often non-i.i.d. across the vehicles. This paper presents a new confidence-based federated distillation method to improve the performance of federated learning for steering angle prediction. Specifically, it proposes the novel use of entropy to determine the predictive confidence of each local model, and then selects the most confident local model as the teacher to guide the learning of the global model. A comprehensive evaluation of vision-based lane centering shows that the proposed approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.

关键词： Forecasting

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SYENet: A Simple Yet Effective Network for Multiple Low-Level vision Tasks with Real-time Performance on Mobile Device

SYENet: A Simple Yet Effective Network for Multiple Low-Leve...

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IEEE/CVF international conference on Computer vision (ICCV)

作者： Gou, Weiran Yi, Ziyao Xiang, Yan Li, Shaoqing Liu, Zibin Kong, Dehui Xu, Ke State Key Lab Mobile Network & Mobile Multimedia Beijing Peoples R China Sanechips Technol Chengdu Peoples R China

ISBN: (纸本)9798350307184

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task- specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only 6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image signal processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge.

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Object Color Identification and Classification using CNN Algorithm and Machine Learning Technique 4

Object Color Identification and Classification using CNN Alg...

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4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Kiruba, Mrs R. Sneha, Ms, V Anuragha, Ms S. Vardhini, Ms S. K. Vismaya, Ms V. Sri Ramakrishna Engn Coll Coimbatore Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

The need for automation in the textile industry is growing rapidly today. Color based object sorting is a highly challenging process to be considered and needs to be addressed. It involves an automated material handling system. It synchronizes the movement of robotic arm to pick up objects moving on a mobile robot. It aims in classifying the colored objects then picking and placing the objects in its respective pre-programmed place. Thereby eliminating the monotonous work done by human, achieving accuracy and speed in the work. The core objective of the project is to propose an intelligent color-based object sorting system using deep learning technique like Convolution Neural Network for extraction of feature embedded with the machine learning algorithm. The two classifiers Random Forest and K-NN algorithm were implemented and studied for better classification. Based on the performance metrics, the Radom Forest algorithm out performs in classification. The project module involves cameras that captures the object's color through the computer vision Library and sends the signal to the controller. The dataset of the captured images has been uploaded and compared with the trained data set. The ESP 32 Module transmit a signal to relay circuit, which then drives the robotic arm's multiple motors to grip the object and position it in the given area. Based on the color observed, the robotic arm goes to the given point, releases the object, and returns to its original position.

关键词： Computer vision Library robotic Arm Convolution Neural Network (CNN) Random Forest (RF) k means Nearest Neighbor (KNN)

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Intelligent Pedestrian Crossing System at Urban Intersection based on Machine vision Intelligent Recognition System 2

Intelligent Pedestrian Crossing System at Urban Intersection...

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2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Cheng, Huiting Chongqing Vocational College of Transportation School of Rail Transportation Chongqing China

ISBN: (纸本)9798350317558

Urban intersections are important places where pedestrians and vehicles meet, and pedestrian crossing safety has become a key issue. An intelligent pedestrian crossing system based on a machine vision intelligent recognition system is proposed as a solution to improve pedestrian traffic safety and street crossing experience. This study uses a machine vision algorithm model, combined with image and video processing technology, to design and implement an intelligent pedestrian crossing system at an urban intersection. Experimental testing and performance evaluation of the system were conducted, focusing on key indicators such as pedestrian detection and recognition accuracy, signal light status recognition accuracy, and system response time. Experimental results show that the pedestrian detection and recognition accuracy of this system is between 88% and 95%. The pedestrian intelligent crossing system shows high accuracy in pedestrian detection and recognition accuracy, and can accurately calibrate the position of pedestrians and Identify their identity information. The system also shows a high accuracy in the signal light status identification task and can accurately determine the current signal light status. In addition, the system has a short reaction time and can take necessary actions and decisions in time to ensure pedestrians cross the street safely. © 2024 IEEE.

关键词： Intelligent Identification System Intelligent Pedestrian Crossing System Machine vision Right-of-Way Allocation

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SYMBIOTIC ARTIFICIAL INTELLIGENCE: ORDER PICKING AND AMBIENT SENSING

SYMBIOTIC ARTIFICIAL INTELLIGENCE: ORDER PICKING AND AMBIENT...

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

作者： Chng, Zhe Ming Tang, Calix Krishnaswamy, Darshan Yang, Haoyang Chopra, Shivang Womack, Jon Starner, Thad Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA

ISBN: (纸本)9798350302615

Using egocentric video and head motion data from 67 order picking tasks (244 picks;149 orders), we learn visual models of the 10 objects picked to fulfill the orders. Boundary segmentations of the four actions (pick, carry, place, carry empty) of order picking had an average test RMSE of 1.11 seconds using computer vision and 5.53 seconds using only head motion (approximate to 39.8 seconds/task). The 10 objects were clustered with 93.8% accuracy using weak supervision provided by the picks (which could occur in any order) specified in the tasks. We apply the 10 resulting models on independent test data to recognize three objects involving 50 tasks (185 picks;98 orders) and 10 objects involving 10 tasks (35 picks;24 orders). Accuracy was up to 90.3% and 69.1%, respectively. We propose order picking as a practical use case of egocentric Symbiotic AI, where ambient sensing is used without explicit supervision to train an agent which can then help the user improve task speed and accuracy.(1)

关键词： ambient AI symbiotic AI wearable sensing egocentric vision

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Improved SAF-FCOS Detection Algorithm for Obstacles with Radar and vision Fusion 15

Improved SAF-FCOS Detection Algorithm for Obstacles with Rad...

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15th international conference on signal processing Systems, ICSPS 2023

作者： Zhou, Zhiwei Zhou, Jianjiang Deng, Kai Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510675056

With the rapid development of automated driving technology, it becomes crucial to accurately detect and recognize targets in complex scenes. Camera sensor detection and recognition accuracy is not high enough, has poor stability, and can not adapt to the target detection of complex scenes. Millimetre wave radar is less affected by the environment and more accurate in speed and distance measurement, but the current mainstream millimeter wave radar sensor point cloud is sparse, so it can not identify the target feature information. Therefore, the fusion of radar and vision is gradually becoming a mainstream solution for accurate obstacle detection. This paper presents a novel parallel attention fusion module (PAFM) to enhance the performance of the SAF-FCOS target detection network. By incorporating depth-separable convolution and parallel dual attention modules, the RetinaNet network of SAF-FCOS is enhanced to effectively integrate information from upper and lower feature maps of diverse scales. Compared to the direct summation approach, PAFM enables the network to selectively focus on image regions of interest, both in terms of channel and spatial dimensions, without introducing excessive algorithmic complexity. Experimental results on the NuScenes dataset demonstrate that the proposed improved network achieves superior performance compared to the original network. © 2024 SPIE.

关键词： Complex networks

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Prediction of Handwritten Classification using CNN Techniques 9

Prediction of Handwritten Classification using CNN Technique...

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9th international conference on signal processing and Communication, ICSC 2023

作者： Sanu, Kumar Bhandari, Rahul Computer Science and Engineering Chandigarh University Chandigarh India

ISBN: (纸本)9798350383201

Today's computer vision industry makes extensive use of image recognition. A popular method of image recognition is digit recognition. The recognition of handwritten numbers is one of the most well-known difficulties in computer vision and machine learning applications. In essence, this model proposes an online approach for recognizing handwritten digits (HDR) by utilizing convolutional neural networks (CNN).As a training sample, the technique uses the MINST dataset, which contains centered 28x28 gray scale images of handwritten numbers. It has 10,000 test cases and 60,000 training examples. This paper shows the accuracy rate and loss of the model. Lastly, execute the trials using a variety of random handwritten 28x28 pixel digits. © 2023 IEEE.

关键词： Computer vision

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