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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24757 条记录，以下是511-520 订阅

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Robot Learning from Multiple Demonstrations Based on Generalized Gaussian Mixture Model 28

Robot Learning from Multiple Demonstrations Based on General...

引用

28th international conference on automation and Computing, ICAC 2023

作者： Dong, Jiale Si, Weiyong Wang, Ning Yang, Chenguang School of Automation Science and Engineering South China University of Technology Guangzhou China University of the West of England Bristol Robotics Lab BristolBS161QY United Kingdom

ISBN: (纸本)9798350335859

The quality of skills learned by robots from demonstrations depends on the level of demonstration by the instructor, while models such as Dynamic Motion Primitives (DMP) are extremely sensitive to the quality of demonstrations because they can only learn from a single demonstration. In this paper, we propose a method of learning from multiple demonstration. First, we model multiple demonstration data by generalized Gaussian mixture models (GMMs), then generate a smooth trajectory by the Gaussian mixture regression (GMR), and finally, the generated trajectory is modeled by DMP. This method can capture features from multiple demonstrations of the same task, reducing the impact of incorrect demonstrations on skill learning. Additionally, the generalized Gaussian mixture model has strong data fitting ability, enabling robots to learn smooth and high-quality motion skills. Finally, the effectiveness of the proposed method was verified by performing a Chinese character writing task on a Baxter robot. © 2023 IEEE.

关键词： Demonstrations

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A Cube Workpiece Positioning Method Based on Point Structured Light Scanning 5

A Cube Workpiece Positioning Method Based on Point Structure...

引用

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

作者： Cheng, Li Xu, Chaohai Chi, Shichun Mao, Xinyu Zhu, Sijun School of Mechanical Engineering Shenyang University Shenyang China Dep. Intelligent Detection and Equipment Shenyang Institute of Automation CAS Shenyang China AVIC XI'AN Aircraft Industry Group Company LTD Xi'an China

ISBN: (纸本)9798350326369

In the automation upgrading process of traditional labor-intensive industries, positioning method of workpiece affects the automated operation quality, efficiency, and system cost directly. Traditional workpiece positioning methods based on image vision and line structured light usually require higher professional knowledge or cost, which increase the difficulty for widely application. Therefore, this paper proposes a workpiece positioning method based on point structured light scanning for cube-shaped workpieces. Positioning accuracy is analyzed and the feasibility of this method is verified through numerical example. The result of this research provides a precise and low-cost workpiece positioning method for the automation upgrade of traditional labor-intensive industries. © 2023 IEEE.

关键词： automation

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Drives and Converters, alsox Known in robotics & automation

Drives and Converters, alsox Known in Robotics & Automation

引用

2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ajit Ranganathaswamy, M.K. Alam, Intekhab Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Although it has many design restrictions, the discipline of power electronics is essential to robotics and automation applications. Key design restrictions are examined in this study, including those related to dynamic range, power electronics dependability, precision engineering, productivity, and motion control standards. The report addresses the fundamental problems and their applicability to developing technology for each constraint, emphasising the difficulties that power electronic systems may face in the future. © 2023 IEEE.

关键词： automation Dependability Dynamic Range Motion Control Power Electronics Precision Engineering Productivity robotics Self-Sensing methods Sensorless Control Thermal Control

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Stiffness Recognition Method Based on Modal Vibration for Serial Robotic Systems 24

Stiffness Recognition Method Based on Modal Vibration for Se...

引用

24th international conference on Control, automation and Systems

作者： Liang, Siyu Liu, Guangjun Helian, Bobo Wang, Yunfei Tongji Univ Sch Mech Engn Shanghai 200092 Peoples R China Karlsruhe Inst Technol Inst Mobile Machines D-76131 Karlsruhe Germany Shanghai Engn Res Ctr Safety Intelligent Control Shanghai 200032 Peoples R China

ISBN: (纸本)9798331517939;9788993215380

Automatic drilling systems based on 6-DOF robots are widely used in aviation manufacturing and other fields. However, as traditional serial robots with weak stiffness, the influence of stiffness on the machining system cannot be ignored in loading or working. Thus, it is significant to recognize the stiffness characteristic of robotic drilling systems precisely and rapidly. This paper presents a comprehensive study of the mechanical properties of serial manipulators, specifically focusing on stiffness and vibration theories. A novel model, the serial rigid links connected by elastic joints (SRLCEJ) with Spong's joints, is introduced to describe the pose deformations under external forces, considering the links as rigid bodies for equivalent joint stiffness conversion. The paper further proposes a dynamic stiffness recognition method, integrating modal vibration theory. The efficacy and accuracy of the proposed theories and methods are validated through simulation results. The findings confirm that joint stiffness parameters can be obtained by vibration responses. In this method, the system stiffness properties can be estimated by neither setting any additional sensor on the end effector nor undertaking any other test when the robot reaches a new posture, which would be more convenient to operate. and may play an important role in robot dynamic stiffness control, online measurement, and real-time error compensation.

关键词： Serial manipulator robotics Stiffness Machine vision Vibration

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Predicting the effects of oscillator-based assistance on stride-to-stride variability of Parkinsonian walkers 39

Predicting the effects of oscillator-based assistance on str...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Otlet, Virginie Ronsse, Renaud UCLouvain Inst Mech Mat & Civil Engn B-1348 Louvain La Neuve Belgium UCLouvain Inst Neurosci B-1348 Louvain La Neuve Belgium UCLouvain Louvain Bion B-1348 Louvain La Neuve Belgium

ISBN: (纸本)9781728196817

Parkinson's disease is a severe neurodegenerative disorder that affects sensorimotor control. In particular, several gait impairments are reported, including a decrease of long-range autocorrelations in stride duration time series. This complex statistics is potentially a biomarker of the risk of falling. This paper aims at developing model-based predictions about the loss of long-range autocorrelations in the gait of Parkinsonian patients, and how these autocorrelations can be restored by an oscillator-based walking assistance. Using a Super Central Pattern Generator model coupled with an adaptive oscillator, we show that this type of assistance has the potential to improve long-range autocorrelations in time series of Parkinsonian walkers. This requires however to tune the adaptive oscillator with slow learning gains, raising challenges for porting this method to an actual device.

关键词： Legged locomotion Adaptation models Parkinson's disease Biological system modeling Time series analysis Predictive models Robot sensing systems

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 IEEE.

关键词： Deep reinforcement learning

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AirLoop: Lifelong Loop Closure Detection 39

AirLoop: Lifelong Loop Closure Detection

引用

IEEE international conference on robotics and automation (ICRA)

作者： Gao, Dasong Wang, Chen Scherer, Sebastian Carnegie Mellon Univ Robot Inst Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781728196817

Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have received increasing attention. Although they normally benefit from training on datasets that are diverse and reflective of the environments, new environments often emerge after the model is deployed. It is therefore desirable to incorporate the data newly collected during operation for incremental learning. Nevertheless, simply finetuning the model on new data is infeasible since it may cause the model's performance on previously learned data to degrade over time, which is also known as the problem of catastrophic forgetting. In this paper, we present AirLoop, a method that leverages techniques from lifelong learning to minimize forgetting when training loop closure detection models incrementally. We experimentally demonstrate the effectiveness of AirLoop on TartanAir, Nordland, and RobotCar datasets. To the best of our knowledge, AirLoop is one of the first works to achieve lifelong learning of deep loop closure detectors.

关键词： Training Simultaneous localization and mapping automation Atmospheric modeling Detectors Data models Robustness

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Examining Two-Stage Hybrid Flow Shop models: Evaluating the Effects of Release Date Constraints

Examining Two-Stage Hybrid Flow Shop Models: Evaluating the ...

引用

2024 international conference on Control, automation and Diagnosis, ICCAD 2024

作者： Nabli, Zouhour Khalfallah, Soulef Korbaa, Ouajdi University of Sousse Mars Research Lab LR17ES05 Sousse Tunisia

ISBN: (纸本)9798350361025

In this study, our focus is on exploring the implications of incorporating release date constraints into both mathematical models and heuristic methods designed to tackle the hybrid flow shop problem with dedicated machines. Our primary aim remains centered on minimizing the makespan, a fundamental objective in production scheduling. Through meticulous investigation, we aim to assess how the introduction of release date constraints influences the performance and efficacy of these models and heuristics. By analyzing the resultant impact on solution quality and computational efficiency, we endeavor to gain deeper insights into the complex dynamics of this optimization problem. © 2024 IEEE.

关键词： Heuristic methods

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Path-Aware Graph Attention for HD Maps in Motion Prediction 39

Path-Aware Graph Attention for HD Maps in Motion Prediction

引用

IEEE international conference on robotics and automation (ICRA)

作者： Da, Fang Zhang, Yu QCraft Inc Beijing Peoples R China

ISBN: (纸本)9781728196817

The success of motion prediction for autonomous driving relies on integration of information from the HD maps. As maps are naturally graph-structured, investigation on graph neural networks (GNNs) for encoding HD maps is burgeoning in recent years. However, unlike many other applications where GNNs have been straightforwardly deployed, HD maps are heterogeneous graphs where vertices (lanes) are connected by edges (lane-lane interaction relationships) of various nature, and most graph-based models are not designed to understand the variety of edge types which provide crucial cues for predicting how the agents would travel the lanes. To overcome this challenge, we propose Path-Aware Graph Attention, a novel attention architecture that infers the attention between two vertices by parsing the sequence of edges forming the paths that connect them. Our analysis illustrates how the proposed attention mechanism can facilitate learning in a didactic problem where existing graph networks like GCN struggle. By improving map encoding, the proposed model surpasses previous state of the art on the Argoverse Motion Forecasting dataset, and won the first place in the 2021 Argoverse Motion Forecasting Competition.

关键词： Costs Roads Computational modeling Semantics Computer architecture Predictive models Encoding

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Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering 39

Trajectory Distribution Control for Model Predictive Path In...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yin, Ji Zhang, Zhiyuan Theodorou, Evangelos Tsiotras, Panagiotis Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781728196817

This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efficient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.

关键词： Degradation Uncertainty Navigation Predictive models Land vehicles Trajectory Numerical models

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