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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321-330 订阅

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Model-Free Feedback control of a Hybrid-driven Soft Manipulator 30

Model-Free Feedback Control of a Hybrid-driven Soft Manipula...

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30th international conference on Mechatronics and Machine vision in Practice, M2VIP 2024

作者： Fu, Xin Zhang, Daohui Li, Kai Chu, Yaqi Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350391916

Soft robots offer high compliance and flexibility, enhancing their human and environmental interaction capabilities. However, complex modeling and control hinder the adoption of soft robots. this paper presents a novel model-free control approach for a hybrid-driven soft manipulator. It utilizes a model-free control method to estimate the Jacobian matrix in real time via end position feedback of the soft manipulator. To mitigate operational noise, Huber's M-estimation technique is incorporated. Furthermore, an inner and outer loop motion controller is designed to effectively govern the soft manipulator and accelerate the pneumatic drive's response within the hybrid drive system. the proposed control approach is demonstrated through experiments. the results substantiate its capability to withstand external disturbances and environmental noise, enabling efficient soft manipulator control. © 2024 IEEE.

关键词： Jacobian matrices

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Person Recognition and Authenticity through Facial Analysis Combining LSTM and Siamese Networks

Person Recognition and Authenticity Through Facial Analysis ...

引用

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

作者： Ionel Gabriel Zahia Loretta Ichim Dan Popescu Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

the automatic recognition of real people is particularly important in many fields of activity. the article proposes a complex system for recognizing people taking into account possible fraud attacks. A Long Short-Term Memory neural network was used to detect possible frauds in the video sequences, and to recognize the person in the case after validation as a real person, two Siamese neural networks implemented with VGG 16 and ResNet 50 were tested. the obtained results were compared for Euclidean distance, Cosine distance, and for two training methods: Contrastive Loss and Triple Loss. the best results were obtained for VGG 16, Cosine distance, and Triple Loss.

关键词： Training Convolution Neural networks Video sequences Euclidean distance Fraud Convolutional neural networks Long short term memory Robots Identification of persons

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6D Pose Estimation for vision-guided Robot Grasping Based on Monocular Camera 6

6D Pose Estimation for Vision-guided Robot Grasping Based on...

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6th international conference on robotics, control and automation Engineering, RCAE 2023

作者： Wang, Shiman Liu, Jianxin Lu, Qiang Liu, Zhen Zeng, Yongjie Zhang, Dechang Chen, B.O. Xihua University Chengdu China Nagasaki Institute of Applied Science Nagasaki Japan

ISBN: (纸本)9798350315301

Enhancing the intelligence of grasping operations through visual empowerment is currently a hot research topic in the field of robotics, where pose estimation is an important component. However, existing methods that solely rely on RGB images mostly employ direct methods or indirect methods using two-stage/multistage network predictions. Estimating the translation and rotation components of pose solely through a single network model does not yield optimal solutions. In this paper, different networks were used to predict the translation and rotation components, and convolutional block attention module (CBAM) and Pyramid Pooling Module (PPM) were introduced to address the issues that features decay in the backbone residual network after multiple layers of convolution and the insufficient utilization of feature information. Furthermore, in the rotation prediction branch, the problem of inaccurate recognition caused by weakened details after multiple layers of convolution was addressed by incorporating pyramid pooling into the network to capture richer detail and contextual information. Experimental tests were conducted on the publicly available Linemod dataset and in real environments. the results demonstrate that the proposed network exhibits a significant improvement of 2.76% in the most common ADD metric compared to a coordinate-based disentangled pose network (CDPN). It also achieves a 1 % improvement in the 5°, 5cm metrics and comparable performance in the Proj.2D metric with a 0.2% increase. © 2023 IEEE.

关键词： Convolution

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Design of Servo control System for Mobile Robot Based on UAV Global vision 24

Design of Servo Control System for Mobile Robot Based on UAV...

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4th international conference on robotics and control Engineering, RobCE 2024

作者： Ji, Peng Dou, Yonglin Pang, Yanxu Wang, Ziqi Ma, Fengying School of Information and Automation Engineering Qilu University of Technology Shandong Academy of Sciences Shandong Province Jinan City China

ISBN: (纸本)9798400716782

When ground reconnaissance mobile robots perform tasks alone, they can only obtain ground view perception, which leads to the inability to globally optimize, resulting in local rather than global optimal solutions. Inspired by this, a servo control system for mobile robots based on UAV global vision is designed in this paper. Firstly, acquire global visual images and process them, selecting target points via mouse under global vision. Secondly, plan an obstacle avoidance path to reach the target point using artificial potential field method, and smooth the path using B-spline curves. then, establish the kinematic model of the mobile robot and use the LQR algorithm to enable the robot to track the path. Finally, experimental tests were conducted via the Webots simulation platform to verify the feasibility of the system. Experimental results show that the system can ensure high accuracy and smooth path tracking, and has strong cooperative control, effectively addressing the single visual problem of ground mobile robots. Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mobile robots

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18th international Symposium of robotics Research, ISRR 2022

18th International Symposium of Robotics Research, ISRR 2022

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18th international Symposium of robotics Research, ISRR 2022

ISBN: (纸本)9783031255540

the proceedings contain 37 papers. the special focus in this conference is on robotics Research. the topics include: 6N-DoF Pose Tracking for Tensegrity Robots;scale-Invariant Fast Functional Registration;towards Mapping of Underwater Structures by a Team of Autonomous Underwater Vehicles;contact-Implicit Planning and control for Non-prehensile Manipulation Using State-Triggered Constraints;mechanical Search on Shelves with Efficient Stacking and Destacking of Objects;multi-object Grasping in the Plane;parameter Estimation for Deformable Objects in Robotic Manipulation Tasks;place-and-Pick-Based Re-grasping Using Unstable Placement;safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces;cooperation of Assistive Robots to Improve Productivity in the Nursing Care Field;learning Agile, vision-Based Drone Flight: From Simulation to Reality;Adaptive Radiation Survey Using an Autonomous Robot Executing LiDAR Scans in the Large Hadron Collider;reference-Free Learning Bipedal Motor Skills via Assistive Force Curricula;ball-and-Socket Joint Pose Estimation Using Magnetic Field;bulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework;Optimization-Based Online Flow Fields Estimation for AUVs Navigation;flying Hydraulically Amplified Electrostatic Gripper System for Aerial Object Manipulation;hybrid Quadratic Programming - Pullback Bundle Dynamical Systems control;riemannian Geometry as a Unifying theory for Robot Motion Learning and control;ensured Continuous Surveillance Despite Sensor Transition Using control Barrier Functions;nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots;Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning;reactive Anticipatory Robot Skills with Memory;ROSE: Robust State Estimation via Online Covariance Adaption;what Can Algebraic Topology and Differential Geometry Teach Us About Intrinsic Dynamics and G

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Event-Triggered Formation control of Heterogeneous Multiagent Systems Under FDI Attacks and External Disturbances

Event-Triggered Formation Control of Heterogeneous Multiagen...

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

作者： Yongran Zhi Huijin Fan Lei Liu Bo Wang National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In this article, the leader-follower time-varying output formation control problem of heterogeneous multiagent systems(MASs) in the presence of continuous false data injection (FDI) attacks and unknown external disturbances is investigated. A hierarchical event-triggered control frame, including communication layer and controller layer, is constructed. In the communication layer, a distributed event-triggered estimator is proposed to estimate the leader's state and release the communication burden. In the controller layer, a robust adaptive state observer is firstly designed to estimate the follower's state with eliminating the influence of continuous FDI attacks and unknown external disturbances, then an output feedback event-triggered controller is developed to track the estimated leader's state. the proposed framework not only reduces the transmission burden but also eliminates the impact of attacks and disturbances. Finally, an illustrative simulation is provided to demonstrate the effectiveness of the proposed approach.

关键词： automation Event detection Observers Formation control Time-varying systems Output feedback Robots Multi-agent systems

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Distributed Dissipative Filtering with Consensus on Estimation: A Two-Dimensional System Method

Distributed Dissipative Filtering with Consensus on Estimati...

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

作者： Shufen Ding Deyuan Meng Seventh Research Division Beihang University (BUAA) Beijing P. R. China School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing P. R. China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this paper deals with the problem of distributed estimation with consensus by utilizing a two-dimensional (2-D) system approach. the consensus protocol is employed as the information fusion strategy, enabling local communication among neighboring nodes to drive their estimates toward consensus, and the distributed dissipative filter is designed on each node. It is shown that the estimation error dynamics is asymptotically stable and meets the prescribed dissipativity performance. Moreover, by incorporating consensus updates in an additional time dimension, the estimator parameters are determined through a constructed 2-D system approach, thereby reducing the design complexity. Simulations demonstrate the validity of the proposed algorithm.

关键词： Estimation error Network topology Two-dimensional displays Heuristic algorithms Process control Filtering algorithms Drives Robot sensing systems Information filters Topology

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Autonomous Navigation of Robots on Roads using vision-Based Techniques for Road Identification and Lane Detection

Autonomous Navigation of Robots on Roads using Vision-Based ...

引用

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

作者： Samadhan Manore M Sasikumar Abhijeet Suryawanshi CDAC Mumbai

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Autonomous robot navigation on Indian roads presents unique challenges, particularly within campuses limited by weak GPS signals due to surrounding buildings and trees. this paper proposes a cost-effective solution for navigating such environments using a DC-powered robot vehicle within the campus of CDAC Mumbai. the approach leverages computer vision for lane detection and road demarcation, eliminating reliance on GPS. the lane detection approach utilises binary mask generation, edge detection, and Hough transform, while road demarcation employs colour filtering, contouring, and moments from the OpenCV library. Experimental results demonstrate a 72% navigation accuracy with minimal false detections. this algorithm is applicable for real-time scenarios, and enables autonomous vehicle navigation using minimal and cost-effective sensors, making it suitable for environments with obstructed GPS signals.

关键词： Navigation Lane detection Roads Transforms Robot sensing systems Real-time systems Sensors Robots Global Positioning System Autonomous robots

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control of van de Vusse Reactor using Deep Reinforcement Learning

Control of van de Vusse Reactor using Deep Reinforcement Lea...

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

作者： Rahul Yadav Ankalugari M U Abuthahir Nabil Magbool Jan Ajin George Joseph Department of Chemical Engineering Indian Institute of Technology Tirupati Andhra Pradesh India Department of Computer Science and Engineering Indian Institute of Technology Tirupati Andhra Pradesh India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

van de Vusse reaction system in a continuous stirred tank reactor is a benchmark example to study the control of nonlinear non-minimum phase systems. Although different control strategies exist to deal with such systems, the controller's performance strongly depends on the accuracy of the process model. In this work, we develop a model-free, deep reinforcement learning-based control strategy to perform setpoint tracking control. In particular, we develop a deep Q-learning based controller and demonstrate the tracking performance for different reference trajectories of the desired product concentration. Furthermore, we illustrate the disturbance rejection ability of the proposed deep RL controller.

关键词： Q-learning Accuracy Process control Predictive models control systems Deep reinforcement learning Trajectory Continuous-stirred tank reactor Inductors Robots

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A Lidar-vision Fusion Target Detection Model for Low-Light Environments

A Lidar-Vision Fusion Target Detection Model for Low-Light E...

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

作者： Jing Wang Shuai Duan Meng Zhou school of Electrical and Control Engineering North China University of Technology Beijing P. R. China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In the field of autonomous driving and specific environments, target detection is a critical task module. Currently, the mainstream approach to target detection is to use deep learning to train specific network models, enabling them to recognize targets. Although many effective network models have been proposed by scholars, there are few target detection networks tailored for low-light environments. In practical applications, complex lighting changes can lead to decreased accuracy in target detection. this paper combines a low-light enhancement network with a LiDAR-camera fusion target detection network to achieve target detection in low-light environments and validates the algorithm using the Nuscenes benchmark dataset. the experimental results demonstrate that the improved network exhibits greater robustness in target detection under complex lighting conditions.

关键词： Deep learning Accuracy Laser radar Target recognition Lighting Object detection Benchmark testing Robustness Robots Autonomous vehicles

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