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共 24967 条记录，以下是4161-4170 订阅

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Enhancing Online Load Model Parameter Identification with Deep Reinforcement Learning: A DDPG-Based Approach

Enhancing Online Load Model Parameter Identification with De...

引用

international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Zihao Liu, Xingwei Huang, Yupeng Hu, Jianxiong Tang, Yi School of Electrical Engineering Southeast University Nanjing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology Beijing China China Electric Power Research Institute Beijing China

ISBN: (纸本)9789819620791

To cope with the complex dynamic characteristics brought about by the sharp increase in penetration of various power electronic devices, online dynamic security analysis in modern power systems has put forward higher requirements for the accuracy of simulation models. Traditional methods for load model parameter identification (PI) struggle with model complexity, computational pressure, and low generalization, which can hinder their effectiveness in online applications. To overcome these challenges, this paper proposes a novel online load model PI method utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. Initially, a composite load model incorporating various distributed energy resources (DERs) is developed as the model basis. Subsequently, a DDPG-based framework for online load model PI is designed, featuring meticulously crafted interface functions to improve both convergence and generalization capabilities. Case studies on the EPRI-36 buses test system demonstrate the proposed method's efficiency and accuracy compared to traditional heuristic optimization methods. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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Design of Two Morphing Robot Surfaces and Results from a User Study On What People Want and Expect of Them, Towards a "Robot-Room"

Design of Two Morphing Robot Surfaces and Results from a Use...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Nithesh Kumar Hsin-Ming Chao Bruno Dantas Da Silva Tassari Elena Sabinson Ian D. Walker Keith E. Green Department of Electrical and Computer Engineering Clemson University Clemson SC USA Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose, examine prototypes of, and collect user input on morphing robotic surfaces, "robot-room" elements that, individually or in combination, change the functionality of the rooms we live in, directly controlled by the room’s occupants engaging with it. Robot-rooms represent an advance in human-robot interaction whereby human interaction is within a machine that physically envelops us. We discuss the motivation for such robot-rooms, present initial work aimed at their physical realization, and report on a user study of 80 participants to learn what people might want of and expect from robot rooms, the results of which will inform both the iterative design of the robot room and the thinking of our community as it grapples with how we want to live with (and "in") robots.

关键词： Productivity Prototypes Human-robot interaction Iterative methods Artificial intelligence Robots

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End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight

End-to-end Reinforcement Learning for Time-Optimal Quadcopte...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Robin Ferede Christophe De Wagter Dario Izzo Guido C.H.E. de Croon Micro Air Vehicle Lab of the Faculty of Aerospace Engineering Delft University of Technology Delft The Netherlands Advanced Concepts Team European Space Agency Noordwijk The Netherlands

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train optimal neural policies. However, a critical hurdle is the sim-to-real gap, often addressed by employing a robust inner loop controller —an abstraction that, in theory, constrains the optimality of the trained controller, necessitating margins to counter potential disturbances. In contrast, our novel approach introduces high-speed quadcopter control using end-to-end RL (E2E) that gives direct motor commands. To bridge the reality gap, we incorporate a learned residual model and an adaptive method that can compensate for modeling errors in thrust and moments. We compare our E2E approach against a state-of-the-art network that commands thrust and body rates to an INDI inner loop controller, both in simulated and real-world flight. E2E showcases a significant 1.39-second advantage in simulation and a 0.17-second edge in real-world testing, highlighting end-to-end reinforcement learning’s potential. The performance drop observed from simulation to reality shows potential for further improvement, including refining strategies to address the reality gap or exploring offline reinforcement learning with real flight data.

关键词： Adaptation models Sensitivity Refining Reinforcement learning Voltage Robustness Task analysis

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methods and Tools for Robotization of Gear Shifting Mechanism for Highly Automated Vehicles

Methods and Tools for Robotization of Gear Shifting Mechanis...

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international Russian automation conference (RusAutoCon)

作者： Ivan A. Rubtsov Department of Innovative Technologies in Mechanical Engineering Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This paper describes models, methods, and tools to support the process of converting a vehicle's mechanical gearbox to robotize it in unmanned driving tasks. The described computer-aided design system (CAD) allows to determine the parameters of the gearbox shifting algorithm used in the control system of a highly automated vehicle (HAVs), depending on the vehicle load, road conditions and parameters of the vehicle itself. The methodology of modeling of the main processes occurring with HAVsduring driving is considered in detail. The developed models allow to determine the parameters of the algorithm of gear shifting for two driving profiles: with minimum fuel consumption and with maximum permissible speed of technological operation. The proposed methods and tools can be useful for designers of HAVscontrol systems, as well as for mechanizers in agriculture.

关键词： Solid modeling Torque Design automation Gears Roads Computational modeling Maintenance Fuels Robots Load modeling

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Spatial or Temporal Signal Considered for Gait Recognition based on Optic-fiber Sensor

Spatial or Temporal Signal Considered for Gait Recognition b...

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IEEE international conference on robotics and Biomimetics

作者： Hanze Jia Hongtao Ma Yang Cheng Linhang Ju Yanggang Feng Wuxiang Zhang School of Mechanical Engineering and Automation Beihang University Beihang Goer(WeiFang) Intelligent Robot Co. Ltd Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University

Gait recognition plays a significant role in the realm of wearable robotics, particularly in facilitating the development of sensors for gait analysis in rehabilitation. In this study, we proposed a novel optic-based sensor for gait recognition and employed four neural network models to extract both temporal and spatial information. Through a comparative analysis of these neural network models across the gait cycle, we investigate the implications of different sensor placement locations and the contributions made by diverse types of information in the context of gait analysis. Through all the models, RNN performed the highest accuracy when the sensor was fixed on the thigh or the calf, and temporal convolutional capsule network(TCCN) performed better when the sensors were fixed both on the thigh and the calf. The results suggested that temporal information could be mainly considered during gait cycle when a single sensor was fixed, and the combination of both temporal and spatial information is critical for accurate gait recognition for multiple sensors placement. This work can provide a new idea for the chosen of recognition models with different sensor placement patterns.

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Degenerate Motions of Multisensor Fusion-based Navigation

Degenerate Motions of Multisensor Fusion-based Navigation

引用

IEEE international conference on robotics and automation (ICRA)

作者： Woosik Lee Chuchu Chen Guoquan Huang Robot Perception and Navigation Group (RPNG) University of Delaware Newark DE USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The system observability analysis is of practical importance, for example, due to its ability to identify the unobservable directions of the estimated state which can influence estimation accuracy and help develop consistent and robust estimators. Recent studies focused on analyzing the observability of the state of various multisensor systems with a particular interest in unobservable directions induced by degenerate motions. However, those studies mostly stay in the specific sensor domain without aiding to extend the understanding to other heterogeneous systems. To this end, in this work, we provide degenerate motion analysis on general local and global sensor-paired systems, offering insights applicable to a wide range of existing navigation systems. Our analysis includes 9 degenerate motion identification including 5 already identified in literature and 4 new motions with both synchronous and asynchronous sensor-pair cases. Comprehensive numerical studies are conducted to verify those identified motions, show the effect of degenerate motion on state estimation, and demonstrate the generalizability of our analysis on various multisensor systems.

关键词： Monte Carlo methods Accuracy Navigation Pose estimation Robot sensing systems Calibration Motion analysis

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Distributed Photovoltaic Power Prediction Based on Spatiotemporal Attention Convolutional Networks under Weather-free Conditions

Distributed Photovoltaic Power Prediction Based on Spatiotem...

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2024 IEEE China international Youth conference on Electrical Engineering, CIYCEE 2024

作者： Peng, Xiaojin Luo, Bingyang Zhu, Guorong School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798331530174

With the massive integration of distributed photovoltaic (PV) systems into the power grid, accurate forecasting of distributed PV power generation is of great significance for the stable operation of power systems. Existing methods often neglect the spatiotemporal correlations among distributed PV systems, making it difficult to effectivelyutilize spatiotemporal feature information to improve power forecasting accuracy. Considering the strong spatiotemporal correlations among distributed PV outputs and the difficulty in obtaining meteorological data, this paper proposes a distributed PV power forecasting method based on Spatial- Time-Attention, Graph Convolutional Network (GCN), and Temporal Convolutional Network (TCN). This method achieves accurate PV power prediction under weather-free conditions based solely on historical power data. First, the spatial-time attention mechanism is used to extract the spatiotemporal dependencies of the distributed PV cluster output. Next, GCN and TCN are utilized to construct a spatiotemporal graph convolutional network for prediction training, obtaining the forecasting results. Finally, a simulation is conducted based on real data from a distributed PV power plant in a certain region of China. The case study results show that the proposed model has higher accuracy compared to traditional deep learning models. © 2024 IEEE.

关键词： Photovoltaics

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Towards Exact Interaction Force Control for Underactuated Quadrupedal Systems with Orthogonal Projection and Quadratic Programming

Towards Exact Interaction Force Control for Underactuated Qu...

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IEEE international conference on robotics and automation (ICRA)

作者： Shengzhi Wang Xiangyu Chu K. W. Samuel Au Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

Projected Inverse Dynamics Control (PIDC) is commonly used in robots subject to contact, especially in quadrupedal systems. Many methods based on such dynamics have been developed for quadrupedal locomotion tasks, and only a few works studied simple interactions between the robot and environment, such as pressing an E-stop button. To facilitate the interaction requiring exact force control for safety, we propose a novel interaction force control scheme for under-actuated quadrupedal systems relying on projection techniques and Quadratic Programming (QP). This algorithm allows the robot to apply a desired interaction force to the environment without using force sensors while satisfying physical constraints and inducing minimal base motion. Unlike previous projection-based methods, the QP design uses two selection matrices in its hierarchical structure, facilitating the decoupling between force and motion control. The proposed algorithm is verified with a quadrupedal robot in a high-fidelity simulator. Compared to the QP designs without the strategy of using two selection matrices and the PIDC method for contact force control, our method provided more accurate contact force tracking performance with minimal base movement, paving the way to approach the exact interaction force control for underactuated quadrupedal systems.

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MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language models using 2D Priors 24

MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large ...

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32nd ACM international conference on Multimedia, MM 2024

作者： Tang, Yuan Han, Xu Li, Xianzhi Yu, Qiao Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology Wuhan China South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (3D-LLMs) also integrate point clouds into LLMs. However, directly aligning point clouds with LLM requires expensive training costs, typically in hundreds of GPU-hours on A100, which hinders the development of 3D-LLMs. In this paper, we introduce MiniGPT-3D, an efficient and powerful 3D-LLM that achieves multiple SOTA results while training for only 27 hours on one RTX 3090. Specifically, we propose to align 3D point clouds with LLMs using 2D priors from 2D-LLMs, which can leverage the similarity between 2D and 3D visual information. We introduce a novel four-stage training strategy for modality alignment in a cascaded way, and a mixture of query experts module to adaptively aggregate features with high efficiency. Moreover, we utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. Extensive experiments show that MiniGPT-3D achieves SOTA on 3D object classification and captioning tasks, with significantly cheaper training costs. Notably, MiniGPT-3D gains an 8.12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800. We are the first to explore the efficient 3D-LLM, offering new insights to the community. Code and weights are available at https://***/TangYuan96/MiniGPT-3D. © 2024 ACM.

关键词： Visual languages

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Generation of Indian Sign Language Letters, Numbers, and Words

Generation of Indian Sign Language Letters, Numbers, and Wor...

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2024 international conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Yadav, Ajeet Kumar Kumar, Nishant Rathna, G.N. Indian Institute of Science Electrical Communication Engineering Banglore Bengaluru India Indian Institute of Science Computer Science and Automation Banglore Bengaluru India Indian Institute of Science Electrical Engineering Banglore Bengaluru India

ISBN: (纸本)9798350360660

Sign language, which contains hand movements, facial expressions and bodily gestures, is a significant medium for communicating with hard-of-hearing people. A well-trained sign language community communicates easily, but those who don't know sign language face significant challenges. Recognition and generation are basic communication methods between hearing and hard-of-hearing individuals. Despite progress in recognition, sign language generation still needs to be explored. The Progressive Growing of Generative Adversarial Network (ProGAN) excels at producing high-quality images, while the Self-Attention Generative Adversarial Network (SAGAN) generates feature-rich images at medium resolutions. Balancing resolution and detail is crucial for sign language image generation. We are developing a Generative Adversarial Network (GAN) variant that combines both models to generate feature-rich, high-resolution, and class-conditional sign language images. Our modified Attention-based model generates high-quality images of Indian Sign Language letters, numbers, and words, outperforming the traditional ProGAN in Inception Score (IS) and Frechet Inception Distance (FID), with improvements of 3.2 and 30.12, respectively. Additionally, we are publishing a large dataset incorporating high-quality images of Indian Sign Language alphabets, numbers, and 129 words. © 2024 IEEE.

关键词： Generative adversarial networks

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