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

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Intuitive Virtual Reality based control of a Real-world Mobile Manipulator

Intuitive Virtual Reality based Control of a Real-world Mobi...

引用

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

作者： Dinh Tung Le Sheila Sutjipto Yujun Lai Gavin Paul

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper presents an integration of Virtual Reality (VR) interfaces with the control system of a realworld mobile manipulator, ultimately facilitating a natural and intuitive method for human-robot interaction. VR's ability to track movements in 3D space and translate performed motions provide an intuitive platform for users to explore and interact with the virtual environment. Coupled with intuitive controls, such as grabbing and pointing, the VR platform provides a compelling advantage that can be used to solve limitations of traditional remote robot teleoperation methods. this paper summarises the system implemented, which includes a simulation of the robot in Unity3d, as well as analyses critical results of accuracy and performance, from experiments with users of various experience levels. the method used for measuring accuracy with a simulated robot presented a utilitarian validation for contrasting the difference between 2D and VR 3D interfaces. Users' performance and experience under various levels of control latency, which is a crucial factor in remote online robot control, were also measured.

关键词： Robots Task analysis three-dimensional displays Two dimensional displays Robot sensing systems Solid modeling Legged locomotion

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Object recognition on humanoids with poveated vision

Object recognition on humanoids with poveated vision

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IEEE-RAS international conference on Humanoid Robots

作者： A. Ude G. Gheng Department of Automatics Joåâef Stefan Institute Ljubljana Slovenia Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot’s central vision. In this paper we describe a system that make use of foveated vision to solve the problem of object recognition on a humanoid robot. the system employs a biologically motivated object representation scheme based on Gabor kernel functions to represent multiple views of objects. We demonstrate how to utilize support vector machines to identify known objects in foveal images using this representation. A mechanism for visual search is integrated into the system to find a salient region and to place an object of interest in the field of view of foveal cameras. the framework also includes a control scheme for eye movements, which are directed using the results of attentive processing in peripheral images.

关键词： Object recognition Robot vision systems Cameras Humanoid robots Eyes robotics and automation Computer vision Lenses Face detection Layout

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Spatial Resolution Metric for Optimal Viewpoints Generation in Visual Inspection Planning 14th

Spatial Resolution Metric for Optimal Viewpoints Generation ...

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14th international conference on Computer vision Systems (ICVS)

作者： Staderini, Vanessa Glueck, Tobias Mecca, Roberto Schneider, Philipp Kugi, Andreas AIT Austrian Inst Technol GmbH Ctr Vis Automat & Control A-1210 Vienna Austria TU Wien Automat & Control Inst A-1040 Vienna Austria

ISBN: (纸本)9783031441363;9783031441370

the automation of visual quality inspection is becoming increasingly important in manufacturing industries. the objective is to ensure that manufactured products meet specific quality characteristics. Manual inspection by trained personnel is the preferred method in most industries due to the difficulty of identifying defects of various types and sizes. Sensor placement for 3D automatic visual inspection is a growing computer vision and robotics area. Although some methods have been proposed, they struggle to provide high-speed inspection and complete coverage. A fundamental requirement is to inspect the product with a certain specific resolution to detect all defects of a particular size, which is still an open problem. therefore, we propose a novel model-based approach to automatically generate optimal viewpoints guaranteeing maximal coverage of the object's surface at a specific spatial resolution that depends on the requirements of the problem. this is done by ray tracing information from the sensor to the object to be inspected once the sensor model and the 3D mesh of the object are known. In contrast to existing algorithms for optimal viewpoints generation, our approach includes the spatial resolution within the viewpoint planning process. We demonstrate that our approach yields optimal viewpoints that achieve complete coverage and a desired spatial resolution at the same time, while the number of optimal viewpoints is kept small, limiting the time required for inspection.

关键词： Ray tracing Sampling density matrix Set coverage problem Spatial resolution Viewpoint planning Visibility matrix

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SGN: Semantic-Guided Net for 3D Semantic Segmentation in Cluttered Scenes

SGN: Semantic-Guided Net for 3D Semantic Segmentation in Clu...

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international conference on Cybernetics, robotics and control (CRC)

作者： Zihao Wan Jianhua Hu Yunkuan Wang Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Inteligence University of Chinese Academy of Sciences Beijing China

3D semantic segmentation is a fundamental task in robotic manipulation and computer vision. However, due to the limitation of heuristic point cloud upsampling methods, the performance of 3D semantic segmentation under cluttered conditions is far from satisfactory, which severely limits the performance of robotic grasping. Specifically, for each query point, previous point-based upsampling methods only choose k nearest neighbors in 3D space as the point set for interpolation, which may cause ambiguity in predicting the category of the query point. To tackle this issue, this paper proposes a novel point cloud semantic segmentation network called Semantic-Guided Net (SGN). the key to SGN is Semantic-Guided Upsampler (SGUS). SGUS make use of both 3D position and semantic information to find the neighbors for interpolation, which reduces the number of irrelevant points from different categories. Furthermore, this paper presents a 3D semantic segmentation dataset called Semantic Cluttered Objects (SCO) to test the proposed module under robotic vision grasping circumstances. Extensive experiments on S3DIS and SCO show the effectiveness of SGN.

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Helium Loss for Robust Keypoints Detection with Convolutional Neural Networks

Helium Loss for Robust Keypoints Detection with Convolutiona...

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automation, control and robotics Engineering (CACRE), international conference on

作者： Haode Huo Tianran Wang Linjie Zhao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

In this paper, a novel loss function for key point detection, named Helium Loss is proposed. All our experiments are based on Ultralytics' YOLOv8n-pose architecture. Initially, this paper analyze various existing loss functions, including L1 loss, L2 loss, and smooth L1 loss. there is a problem that current loss functions do not account for issues related to the quality of dataset annotations and unclear boundaries of targets. To address this challenge, this paper introduce a loss function that incorporates partial trust in annotated key points and utilizes Gaussian distribution to mitigate the impact of manual annotations.

关键词： Training Computer vision Annotations Computational modeling Pose estimation Computer architecture Gaussian distribution

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vision-Based Demonstration Data Collection Platform for Dexterous Robot Manipulation

Vision-Based Demonstration Data Collection Platform for Dext...

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international conference on control, automation and Systems ( ICCAS)

作者： Sunme Park Mingi Jung Seungwon Lee Yeongki Baek Jongbum Park Intelligent Robotics Research Center Korea Electronics Technology Institute Bucheon South Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

this paper presents a vision-based demonstration data collection platform designed to facilitate dexterous robot manipulation for the relocation task. the platform comprises three stages: video capture, pose estimation, and retargeting to the robotic hand, which is then mapped into a simulation environment for behavior learning. To ensure the quality of data for reinforcement learning and imitation learning, various scenarios were considered during the video recording stage. We validated these expert datasets using the DAPG (Demonstration-Augmented Policy Gradient) algorithm, which combines the strengths of reinforcement learning and imitation learning. the successful completion of the relocation task demonstrates the effectiveness of the diverse state data collection.

关键词： Training automation Imitation learning Pose estimation Reinforcement learning Grasping Data collection control systems Robots Video recording

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An improved Faster RCNN for Pedestrian Detection

An improved Faster RCNN for Pedestrian Detection

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control, automation, Power and Signal Processing (CAPS), 2021 international conference on control, automation, Power and Signal Processing (CAPS)

作者： Sweta Panigrahi U.S.N. Raju National Institute of Technology Warangal Warangal Telangana India

ISBN: (纸本)9781665445788

Pedestrian detection plays a pivotal role in applications such as robotics, automated driving, assistive living, and surveillance. the problem of pedestrian detection, although approached by many computer vision researchers is far from solved. the scale, pose, occlusion, illumination, and many such factors affect the performance of the methods. In this work, a modification of the most commonly used deep convolutional neural network model ResNet18 is proposed. the modified CNN structure forms the base of the Faster RCNN model utilized to predict the locations of pedestrians in the image. the proposed method has been improved in terms of the feature map extraction of the image. To evaluate the proposed method, two benchmark datasets INRIA Pedestrian and PASCAL VOC 2012 are considered. the performance metrics used for evaluation are Detection Error Trade-off and Precision-Recall Curve. A statistical analysis is also conducted. the proposed method is compared against state-of-the-art detection methods.

关键词： Measurement Computer vision Statistical analysis Surveillance Lighting Benchmark testing Signal processing

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vision-based Leader-Follower Mobile Robots for Cooperative Object Handling

Vision-based Leader-Follower Mobile Robots for Cooperative O...

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international conference on control, automation and Systems ( ICCAS)

作者： D.U. Jayarathne H.A.K.R. Vidumal S.M.A. Senevirathne P.G. Jayasekara T.D. Lalitharatne Robotics and Control Systems Laboratory University of Moratuwa Katubadda Sri Lanka University of Moratuwa Katubadda Sri Lanka

this paper has addressed the problem of transporting a long object from a given starting location to an end location using leader-follower robot formation in an environment filled with obstacles. Only the leader robot has the authority to make decisions. It possesses the main controller and is capable of navigating through an unknown environment using the data provided by on-board sensors. In the proposed system, a vision-based feedback method is combined with an explicit control method that oversights the virtual trailer between the leader and the follower. the parameters required to maintain a constant distance between the two robots are computed by the leader and transmitted to the follower directly. A fiducial marker is mounted on the front of the follower robot and is captured by a camera attached to the leader robot. Information exchanging methods based only on impedance control can be expensive because of the need of high-level controllers and force/torque sensors for both robots. Furthermore, the visual response scheme adds robustness to the system by reducing the limitations of sole explicit control methods. the proposed control algorithm is implemented on the developed mobile robots. the experimental results suggest that the vision-based feedback method is more effective in cooperate object carrying tasks.

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Real-time door detection in cluttered environments

Real-time door detection in cluttered environments

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IEEE international Symposium on Intelligent control (ISIC)

作者： S.A. Stoeter F. Le Mauff N.P. Papanikolopoulos Center for Distributed Robotics Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA Center for Distributed Robotics Department of Computer Science and Engineering University of Minnesota Minneapolis MI USA

A mobile robot, called a ranger, equipped with a ring of sonars and a color camera is at the center of this research effort. the ranger is part of a fleet of heterogenous robots. Its task is to quickly deploy multiple smaller robots with specialized sensing capabilities, called scouts, into different rooms. In order to achieve the goal, the ranger must be able to safely navigate through potentially unknown urban environments as well as autonomously detect areas into which the scouts should be released. this paper presents the ranger's sensing and control algorithms and reports results from several different environments.

关键词： Robot kinematics Robot sensing systems Sonar navigation Mobile robots Cameras Robot vision systems Humans Robustness Sonar detection Computer science

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Optimal Trajectory Tracking for Cyber-Physical Marine Vessel: Reinforcement Learning Approach

Optimal Trajectory Tracking for Cyber-Physical Marine Vessel...

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

作者： Xiaolei Li Jiange Wang Xinyao Li Jiaqi Yan School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Electrical Engineering Yanshan University Qinhuangdao China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In this paper, we consider the optimal trajectory tracking control problem for a cyber-physical marine surface vessel system with unknown dynamics by using a reinforcement learning (RL) algorithm. the main contributions of the paper are threefold: (1) the dynamics of the marine vessel system considered in this paper is partially unknown. (2) the actor-critic-identifier (ACI) architecture based on fuzzy logical system (FLS) approximation is designed to handle the optimal tracking control problem; (3) the same Bellman error is chosen to update the actor and critic FLS weights at the same time. To be more specific, by using FLS approximating the unknown optimal value function, optimal policy, and partial unknown dynamic, a reinforcement learning algorithm is used to solve the optimal tracking problem of the marine surface vessel system. By designing a fuzzy identifier, the actor-critic weights are adaptively updated simultaneously. the rigorous proof is given to verify the correctness of the algorithm.

关键词： Fuzzy logic Trajectory Heuristic algorithms Optimal control Nonlinear dynamical systems Trajectory tracking Reinforcement learning

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