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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5311 条记录，以下是231-240 订阅

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Proceedings of the 2019 4th international conference on robotics, control and automation, ICRCA 2019 - Workshop 2019 the 4th international conference on robotics and Machine vision, ICRMV 2019

Proceedings of the 2019 4th International Conference on Robo...

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2019 4th international conference on robotics, control and automation, ICRCA 2019 and its Workshop of 2019 4th international conference on robotics and Machine vision, ICRMV 2019

ISBN: (纸本)9781450371834

the proceedings contain 46 papers. the topics discussed include: a sampling selection method for multi-rate sampling control in distributed model predictive control;evaluation of manufacturing capability for the job shop by combining the entropy weight method with grey relational analysis;identification of clamps looseness based on multi-scale convolutional neural network for hydraulic pipelines;optimization of position and pose estimation by using epipolar line segmentation optimization BA algorithm;obstacle detection for assisting navigation of visually impaired people based on segmentation process;design of fall detection system using computer vision technique;a meter pointer detection method based on window function scanning;and dynamic performance analysis and design for a fluidlastic isolator.

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Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline 17

Automated Quantification of Traffic Particulate Emissions vi...

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17th international conference on control, automation, robotics and vision (ICARCV)

作者： Ho, Kong Yuan Lim, Chin Seng Kattar, Mathena A. Boppana, Bharathi Yu, Liya Ooi, Chin Chun Natl Univ Singapore Dept Civil Engn Singapore Singapore Natl Univ Singapore Dept Civil & Environ Engn Singapore Singapore Inst High Performance Comp Singapore Dept Fluid Dynam Singapore Singapore

ISBN: (纸本)9781665476874

Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown that the particulate pollution along major roadways exhibit strong correlation with increased traffic during peak hours, and that reductions in traffic emissions can lead to better health outcomes. However, in many instances, obtaining proper counts of vehicular traffic remains manual and extremely laborious. this then restricts one's ability to carry out longitudinal monitoring for extended periods, for example, when trying to understand the efficacy of intervention measures such as new traffic regulations (e.g. car-pooling) or for computational modelling. Hence, in this study, we propose and implement an integrated machine learning pipeline that utilizes traffic images to obtain vehicular counts that can be easily integrated with other measurements to facilitate various studies. We verify the utility and accuracy of this pipeline on an open-source dataset of traffic images obtained for a location in Singapore and compare the obtained vehicular counts with collocated particulate measurement data obtained over a 2-week period in 2022. the roadside particulate emission is observed to correlate well with obtained vehicular counts with a correlation coefficient of 0.93, indicating that this method can indeed serve as a quick and effective correlate of particulate emissions.

关键词： Computer vision Particulate emissions Vehicular counts Urban traffic Convolutional neural networks

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DR-SLAM: vision-Inertial-Aided Degenerate-Robust LiDAR SLAM Based on Dual Confidence Ellipsoid Oblateness 14

DR-SLAM: Vision-Inertial-Aided Degenerate-Robust LiDAR SLAM ...

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14th IEEE international conference on Cyber Technology in automation, control, and Intelligent Systems, CYBER 2024

作者： Song, Zhixing Wang, Runhua Wu, Songyang Wang, Youwei Tong, Yunze Zhang, Xuebo Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

ISBN: (纸本)9798331506056

In environments with similar geometric structures or sparse features, it is extremely challenging for mobile robots to perform LiDAR simultaneous localization and mapping. this paper presents a new vision-inertial-aided degenerate-robust LiDAR SLAM (DR-SLAM) based on dual confidence ellipsoid oblateness. First, to solve the problem of scale inconsistency between rotation and translation subspaces in traditional degradation judgment algorithms, this paper proposes a novel degradation judgment algorithm based on dual confidence ellipsoid oblateness, which is insensitive to environmental changes. then, we develop a vision-inertial-aided degenerate-robust LiDAR SLAM system. the visual-inertial odometry provides a priori pose, and the LiDAR point cloud is used to update the pose in well-constrained directions. Finally, experimental results in both self-built simulation environments and real-world scenarios demonstrate that DR-SLAM outperforms state-of-the-art methods in terms of accuracy and robustness. © 2024 IEEE.

关键词： Mobile robots

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Research on Compliant control of Collaborative Robots Based on Fuzzy Iterative Algorithm 5

Research on Compliant Control of Collaborative Robots Based ...

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5th international conference on robotics and Computer vision, ICRCV 2023

作者： Sun, Zejun Zhang, Tao Peng, Tianchen Shanghai Engineering Research Center of CNC Equipment Shanghai Electric Group Central Academe Shanghai China

ISBN: (纸本)9798350326369

In human-robot collaboration, the control system must maintain sufficient stability and high precision and ensure robots have good compliance and quick response. In this paper, research is carried out on the compliance control of a collaborative robot with six rotary joints, so that the robot can quickly and accurately realize position tracking and ensure the compliance of its movements. the impedance control algorithm based on joint space was proposed to realize the variable impedance dragging function of the robot. the kinematics model of the robot is established by using Rodrigues' rotation formula, which simplifies the derivation of the kinematics. the fuzzy algorithm has good nonlinearity and can reduce the convergence times of iterations. Based on the dynamics of the robot, the fuzzy iterative algorithm is designed to correct the position error of the robot in trajectory tracking. During the tracking process of the robot, the effectiveness and position-tracking ability of the algorithm are verified. © 2023 IEEE.

关键词： Iterative methods

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A Branching Network for Human Parsing Based on Quantifying Background-Part Relationships *

A Branching Network for Human Parsing Based on Quantifying B...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Tao Yang Juan Du School of Automation Science and Engineering South China University of Technology WuShan Guangzhou Guangdong China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Human parsing is a critical computer vision task that involves segmenting and labeling body parts in images. Due to variations in human poses and clothing, designing effective parsing algorithms is challenging. this paper examines how background impacts parsing results and proposes grouping body parts based on mutual information with the background. We introduce an end-to-end replacement-concatenation network to enhance existing methods and a branching network that refines feature sharing to reduce negative interactions and improve feature learning for different body parts. Experimental results show a significant improvement in parsing effectiveness using our approach.

关键词： Representation learning Image segmentation Computer vision automation Statistical analysis Clothing Labeling Mutual information

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A New Approach on Simultaneous Occupancy Grid Mapping and Particle-based Road Boundary Mapping for Autonomous Vehicles 17

A New Approach on Simultaneous Occupancy Grid Mapping and Pa...

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17th international conference on control, automation, robotics and vision (ICARCV)

作者： Rasyidy, Mukhlas A. Nazaruddin, Yul Y. Widyotriatmo, Augie Inst Teknol Bandung Instrumentat & Control Res Grp Bandung 40132 Indonesia Natl Ctr Sustainable Transportat Technol CRCS Bldg2nd FloorJl Ganesha 10 Bandung 40132 Indonesia

ISBN: (纸本)9781665476874

this paper describes a method of environment mapping for autonomous vehicles that takes the advantages of the high-definition dense local map and sparse global map at the same time. the proposed system consists of two estimators, occupancy grid map estimator that is suitable for online local navigation and the new sparse road boundary map estimator for long-term global mapping. the occupancy grid map is estimated with a common Bayesian inference method. On the other hand, the road boundary map estimator is built based on a simple particle filter algorithm which, compared to grid-based map, is memory efficient as it does not require the algorithm to maintain the occupancy state of each point in space. Both estimations are performed by fusing the information from both LiDAR and camera sensors. Our tests in Carla Simulator have shown that the proposed global road boundary mapping system can construct the global map of the test environment well. the particle filter in the proposed algorithm can also reduce the error of road boundary estimation. Moreover, this paper provides the method to fuse the road boundary map into the grid map, which can improve the accuracy of the grid mapping.

关键词： Autonomous vehicles

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Patrol Agent: An Autonomous UAV Framework for Urban Patrol Using on Board vision Language Model and on Cloud Large Language Model 6

Patrol Agent: An Autonomous UAV Framework for Urban Patrol U...

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6th international conference on robotics and Computer vision, ICRCV 2024

作者： Yuan, Zihao Xie, Fangfang Ji, Tingwei School of Aeronautics and Astronautics Zhejiang University Hangzhou China

ISBN: (纸本)9798331527426

Unmanned Aerial Vehicles (UAVs) used for urban patrols typically require human control or supervision. To enhance the automation of UAV s in this context, we propose the Patrol Agent, which is able to patrol, identify and track a target in a fixed area autonomously without any human intervention. the Patrol Agent employs vision Language Model (VLM) for accurate visual information, object detection model for rough detection about the target, and Large Language Model (LLM) deployed on cloud for analysis and action-deciding. During patrols, the agent uses a lightweight VLM to generate captions of the scenes it observes. these captions are then sent to the LLM on cloud for further analysis which provides responses regarding the danger level of the scene, appropriate actions to take, and the detailed reasons behind these actions. When the agent identifies and tracks a target, it activates the VLM only when the object detection model detects an object corresponding to the target. this approach conserves computing resources and enhances onboard operational speed. the proposed agent can identify and track targets without requiring fine-tuning data or human intervention. It outperforms Visual Question Answering (VQA) models in patrol and uses fewer computing resources compared to agents that solely rely on VLM for tracking. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Data-Driven Optimal control for Continuous- Time Linear Nonzero-Sum Games

Data-Driven Optimal Control for Continuous- Time Linear Nonz...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Hongyang Li Qinglai Wei Ruizhuo Song State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In this paper, the data-driven optimal control problem is studied for continuous-time linear nonzero-sum games. Two kinds of reinforcement learning algorithms, i.e., reinforcement learning algorithm with data-storage based least-square method and reinforcement learning algorithm with filter based least-square method, are presented to obtain the Nash equilibrium solution. the properties of the presented rein-forcement learning algorithms are analyzed. Simulation results show the efficiency of the presented reinforcement learning algorithms.

关键词： automation Simulation Optimal control Reinforcement learning Games Filtering algorithms Nash equilibrium Robots

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TSN-GReID: Transformer-based Siamese Network for Group Re-Identification 17

TSN-GReID: Transformer-based Siamese Network for Group Re-Id...

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17th international conference on control, automation, robotics and vision (ICARCV)

作者： Lu, Xiaoyan Sheng, Weijie Li, Xinde Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing Peoples R China

ISBN: (纸本)9781665476874

Group re-identification (GReID) is an important yet less-studied task. GReID focuses on associating the group images across non-overlapping cameras. the key challenges of GReID include layout variation, membership changes, and occlusion. Most existing methods focus on the group variation but ignore the occlusion that frequently occurs in the group. In practical scenarios, so many activities like conversing, queuing, or fighting consist of groups. To this end, we design a novel Transformer-based Siamese Network for GReID (TSN-GReID) for joint learning of classification and correspondence tasks to learn more robust group features for group layout and membership changes. Furthermore, we put forward an original regrouping random patch module(RRPM) which respectively regroups the member patch embedding and member-level local features to generate group features with improved discrimination ability and more diversified coverage to deal with occlusion. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms state-of-the-art methods by 4.6 % Rank-1 on the CUHK-SYSU Group (CSG) dataset and by 7.1% Rank-1 on the DukeMTMC Group dataset.

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A Comparison of Clustering vs YOLO for Drone Swarm Centroid Detection 8

A Comparison of Clustering vs YOLO for Drone Swarm Centroid ...

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8th international conference on robotics and automation Engineering, ICRAE 2023

作者： Kumari, Nisha Lee, Kevin Ranaweera, Chathurika Barca, Jan Carlo School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9798350327656

the rapid advancement of drones has led to the emergence of drone swarm applications in various domains, including surveillance, search and rescue, and package delivery. Efficient coordination and formation control of drone swarms is crucial for accomplishing complex tasks. the research presented in this paper proposes a novel approach for detecting individual drones in drone swarm formations through the utilisation of the K-means clustering algorithm. the algorithm assigns drones to the nearest centroids, creating cohesive subgroups and optimizing formation quality. To assess its efficacy, a comparative analysis is conducted between the K-means clustering algorithm and you only look once (YOLO) based computer vision detection algorithm. through extensive simulation experiments, it is found that the K-means clustering algorithm outperforms the YOLO-based detection algorithm in terms of formation quality and computational efficiency. It consistently achieves more accurate and stable swarm formations making it suitable for real-time swarm control applications. the results of this presented research open the way for the effective use of drone swarms in a wide range of real-world applications and contribute to the development of cutting-edge swarm control approaches. © 2023 IEEE.

关键词： Computer vision

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