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检索条件"任意字段=International Asia Conference on Informatics in Control, Automation and Robotics"

共 1479 条记录，以下是161-170 订阅

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Integrating Augmented Reality within Digital Twins for Smart Robotic Manufacturing Systems

Integrating Augmented Reality within Digital Twins for Smart...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Toméš Jochman Václav Voltr Václav Kubáček Ondřej Švec Pavel Burget Václav Hlaváč Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This paper explores the integration of augmented reality into robotic manufacturing systems, emphasizing the enhancement of connectivity, real-time data processing, and interactive visual interfaces. Utilizing Hololens 2 headsets equipped with OPC UA clients, the system establishes a direct interface between operators and the digital and physical components of the manufacturing environment. The architecture employs augmented reality to facilitate sophisticated operational control and visualization, ranging from robotic additive manufacturing to complex tasks like robotic sanding and screwing with integrated camera systems. This integration showcases significant advancements in manufacturing processes, allowing operators to engage interactively with the systems, optimize workflows, and improve overall precision and efficiency in a smart factory setting.

关键词： Visualization Manufacturing processes Robot vision systems Cameras Real-time systems User experience Manufacturing Workstations Robots Augmented reality

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Real-Time Adaptive Safety-Critical control with Gaussian Processes in High-Order Uncertain Models

Real-Time Adaptive Safety-Critical Control with Gaussian Pro...

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IEEE international conference on robotics and automation (ICRA)

作者： Yu Zhang Long Wen Xiangtong Yao Zhenshan Bing Linghuan Kong Wei He Alois Knoll Department of Informatics Technical University of Munich Germany Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model’s online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot.

关键词： Adaptation models Adaptive systems Uncertainty Computational modeling Gaussian processes Real-time systems Safety

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Object Detection Algorithms - A Review

Object Detection Algorithms - A Review

引用

international conference on control, Artificial Intelligence, robotics & Optimization (ICCAIRO)

作者： Marek Letavay Michael Bažant Pavel Tuček Asseco Central Europe a.s. Bratislava Slovak Republic Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic

Over several decades, traffic engineers have persistently focused on implementing various measures to enhance the safety of drivers and pedestrians amidst the growing traffic on streets worldwide. This pressing concern stems from the continuous growth in vehicles, pedestrians, and road density. However, with the remarkable advancements in computational power and the simultaneous reduction in the cost of technical equipment, the integration of object detection has emerged as a viable solution, even in real-time applications within the realm of traffic engineering. However, it is essential to note that the impact of computer technology extends far beyond mobility-related issues. Its wide-ranging influence has permeated diverse fields of interest, including medicine, where it aids in diagnostics and treatments, face recognition systems used for security, industrial automation processes, and even artistic endeavors, where it has opened new frontiers of creativity. This indicates object detection technology's vast potential and versatility, with its ability to revolutionize numerous sectors and bring about meaningful societal advancements. This review outlines the chronological progression of the essential YOLO algorithm, highlighting its significant advancements and developments over the years.

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Adaptive control for Triadic Human-Robot-FES Collaboration in Gait Rehabilitation: A Pilot Study

Adaptive Control for Triadic Human-Robot-FES Collaboration i...

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IEEE international conference on robotics and automation (ICRA)

作者： Andreas Christou Antonio J. Del-Ama Juan C. Moreno Sethu Vijayakumar School of Informatics University of Edinburgh UK Electronic Technology Department Rey Juan Carlos University Spain Department of Translational Neuroscience Cajal Institute Spain Alan Turing Institute UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient’s active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient’s performance and their muscles’ fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

关键词： Training Tracking Collaboration Muscles Fatigue Iron Physiology

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A Helical Bistable Soft Gripper Enable by Pneumatic Actuation

A Helical Bistable Soft Gripper Enable by Pneumatic Actuatio...

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IEEE international conference on robotics and automation (ICRA)

作者： Xuanchun Yin Junliang Xie Pengyu Zhou Sheng Wen Jiantao Zhang College of Engineering South China Agricultural University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

There are many instances of helical mechanisms that are used to efficiently grasp different objects with various shapes and sizes in nature. Inspired by the helical grasping in the nature, we proposed a helical bistable soft gripper with high load capacity and energy saving. An off-the-shelf bistable steel shell (BSS) as the stiff element was inserted into a 3D printing soft helical exoskeleton to achieve coiling around and holding the objects without energy consumption. Two air pouches were designed as the actuator to control the transition between the two stable states. To facilitate gripper design, a simplified model of the gripper was conducted, and the geometric parameters of the gripper are listed in a table for reference. The transition pressures between the two stable states were experimentally characterized. Moreover, we conduct experiments to demonstrate the capability of the gripper in two working modes. The gripper exhibits coiling diameters ranging between 40 mm and 60 mm and is successfully attached to various slender objects of different geometries with a maximum holding force of 92.67 N (up to 135.1 times of its mass) in hanging mode. Finally, the gripper was integrated into a robot arm and successfully grasped different objects, and the maximum grasping weight is 221.6 g in the grasping mode.

关键词： Geometry Shape Force Exoskeletons Grasping Pneumatic systems Three-dimensional printing

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Online Social Robot Navigation in Indoor, Large and Crowded Environments

Online Social Robot Navigation in Indoor, Large and Crowded ...

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IEEE international conference on robotics and automation (ICRA)

作者： Steven Silva Nervo Verdezoto Dennys Paillacho Samuel Millan-Norman Juan David Hernández School of Computer Science & Informatics at Cardiff University UK Research Development and Innovation Center of Computer Systems - CIDIS at ESPOL Polytechnic University Ecuador

New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against state-of-the-art approaches in challenging simulated scenarios, and $w$ e also demonstrate its feasibility with the Pepper robot in real-world trials.

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Data-efficient Non-parametric Modelling and control of an Extensible Soft Manipulator

Data-efficient Non-parametric Modelling and Control of an Ex...

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IEEE international conference on robotics and automation (ICRA)

作者： Mohammadreza Kasaei Keyhan Kouhkiloui Babarahmati Zhibin Li Mohsen Khadem School of Informatics University of Edinburgh UK Department of Computer Science University College London UK

Data-driven approaches have shown promising results in modeling and controlling robots, specifically soft and flexible robots where developing physics-based models are more challenging. However, these methods often require a large number of real data, and gathering such data is time-consuming and can damage the robot as well. This paper proposed a novel data-efficient and non-parametric approach to develop a continuous model using a small dataset of real robot demonstrations (only 25 points). To the best of our knowledge, the proposed approach is the most sample-efficient method for soft continuum robot. Furthermore, we employed this model to develop a controller to track arbitrary trajectories in the feasible kinematic space. To show the performance of the proposed approach, a set of trajectory-tracking experiments has been conducted. The results showed that the robot was able to track the references precisely even in presence of external loads (up to 25 grams). Moreover, fine object manipulation experiments were performed to demonstrate the effectiveness of the proposed method in real-world tasks. Finally, we compared its performance with common data-driven approaches in seen/useen-before trajectory tracking scenarios. The results validated that the proposed approach significantly outperformed the existing approaches in unseen-before scenarios and offered similar performance in seen-before scenarios.

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Topology-Based MPC for Automatic Footstep Placement and Contact Surface Selection

Topology-Based MPC for Automatic Footstep Placement and Cont...

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IEEE international conference on robotics and automation (ICRA)

作者： Jaehyun Shim Carlos Mastalli Thomas Corbères Steve Tonneau Vladimir Ivan Sethu Vijayakumar School of Informatics University of Edinburgh U.K. Institute of Sensors Signals and Systems School of Engineering and Physical Sciences Heriot-Watt University U.K. Touchlab Limited U.K.

State-of-the-art approaches to footstep planning assume reduced-order dynamics when solving the combinatorial problem of selecting contact surfaces in real time. However, in exchange for computational efficiency, these approaches ignore joint torque limits and limb dynamics. In this work, we address these limitations by presenting a topology-based approach that enables model predictive control (MPC) to simultaneously plan full-body motions, torque commands, footstep placements, and contact surfaces in real time. To determine if a robot's foot is inside a contact surface, we borrow the winding number concept from topology. We then use this winding number and potential field to create a contact-surface penalty function. By using this penalty function, MPC can select a contact surface from all candidate surfaces in the vicinity and determine footstep placements within it. We demonstrate the benefits of our approach by showing the impact of considering full-body dynamics, which includes joint torque limits and limb dynamics, on the selection of footstep placements and contact surfaces. Furthermore, we validate the feasibility of deploying our topology-based approach in an MPC scheme and explore its potential capabilities through a series of experimental and simulation trials.

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Hybrid Deep Learning with Attention Mechanism based Health State Intelligent Diagnosis of Lithium-Ion Batteries

Hybrid Deep Learning with Attention Mechanism based Health S...

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international conference on control, automation and Diagnosis (ICCAD)

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvédère Tunis Higher Institute of Information and Communication Technologies Hammam Chatt Tunisia CRETn Borj-Cedria Photovoltaic Laboratory (LPV) RISC Le Belvédère ENIT University of El Manar Tunis Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

With the increasing utilization of lithium-ion batteries in electric vehicles, substantial research has been undertaken to ensure the secure and dependable performance of the Battery Management System (BMS). The precise estimation of State of Health (SOH), a pivotal function of the BMS, is critical for the safe operation of electric vehicles. Addressing this challenge, we present a time series model tailored for predicting the State of Health (SOH) of Li-ion batteries. Our approach combines a CNN with a Bidirectional Long Short-Term Memory (BI-LSTM) architecture, enhanced by an attention mechanism. Experimental results demonstrate that this enhanced LSTM approach notably enhances prediction accuracy. The results from this study offer a promising solution for enhancing the reliability and performance of electric vehicle battery systems, ultimately contributing to the widespread adoption and sustainability of electric transportation.

关键词： Lithium-ion batteries Deep learning Accuracy Time series analysis Transportation Predictive models Prediction algorithms

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Flexible Target Grasping Strategy of Industrial Robot Based on Eye-in-Hand 3D Machine Vision

Flexible Target Grasping Strategy of Industrial Robot Based ...

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robotics and Technologies for Industrial automation (ROBOTHIA), IEEE international conference on

作者： Zhang XiaoYang Muhammad Azmi Ayub Fazlina Ahmat Ruslan Shuzlina Abdul-Rahman Sukarnur Che Abdullah College of Engineering UiTM Shah Alam Shah Alam Selangor Malaysia College of Computing Informatics and Media UiTM Shah Alam Shah Alam Selangor Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Object grasping is the most common application direction of robots, and machine vision technology is also the most common application of grasping. With the deepening of the research of machine vision technology, the initial application in static target capture has gradually expanded to some moving target tracking and capture. Estimating and predicting the moving state of a target by detecting its position is a common method for obtaining a stable moving target. Once the target motion changes position due to unpredictable interference, the original predicted position will be invalid. Therefore, it is necessary to constantly locate and track the target and judge the deviation, so as to constantly approach the new target position and finally achieve the capture. In this paper, we utilize the open-source Robot Operating System (ROS) platform to investigate dynamic grasping strategies based on visual feedback. In this paper, we use ROS, based on UR5 robot arm and RealSense D435i camera, based on the eye-in-hand mode, to study the dynamic grasping strategy based on visual feedback. A visual feedback control strategy based on fixation approximation is designed to enable the grasping of moving targets on a conveyor belt. Finally, it is verified that the machine vision-driven feedback capturing algorithm substantially meets the practical application requirements of engineering, demonstrating high feasibility and accuracy.

关键词： Visualization Target tracking Three-dimensional displays Service robots Machine vision Operating systems Robot vision systems Grasping Object detection Industrial robots

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