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检索条件"主题词=Robotics and automation"

共 11731 条记录，以下是121-130 订阅

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MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending

MMPI: a Flexible Radiance Field Representation by Multiple M...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Yuze He Peng Wang Yubin Hu Wang Zhao Ran Yi Yong-Jin Liu Wenping Wang Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China Department of Computer Science The University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Texas A&M University USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI), for high-quality view synthesis of complex scenes. MPI with Normalized Device Coordinate (NDC) parameterization is widely used in NeRF learning for its simple definition, easy calculation, and powerful ability to represent unbounded scenes. However, existing NeRF works that adopt MPI representation for novel view synthesis can only handle simple forward-facing unbounded scenes (e.g., the scenes in the LLFF dataset), where the input cameras are all observing in similar directions with small relative translations. Hence, extending these MPIbased methods to more complex scenes like large-range or even 360-degree scenes is very challenging. In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes. Our key idea is to encode the neural radiance field with multiple MPIs facing different directions and blend them with an adaptive blending operation. For each region of the scene, the blending operation gives larger blending weights to those advantaged MPIs with stronger local representation abilities while giving lower weights to those with weaker representation abilities. Such blending operation automatically modulates the multiple MPIs to appropriately represent the diverse local density and color information. Experiments on the KITTI dataset and ScanNet dataset demonstrate that our proposed MMPI synthesizes high-quality images from diverse camera pose distributions and is fast to train, outperforming the previous fast-training NeRF methods for novel view synthesis. Moreover, we show that MMPI can encode extremely long trajectories and produce novel view renderings, demonstrating its potential in applications like autonomous driving. Our demo video is available at https://***/watch?v

关键词： Image color analysis Neural radiance field Cameras Rendering (computer graphics) Reliability engineering Trajectory robotics and automation

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm.

关键词： Patents Vocabulary Technological innovation Accuracy Semantics Predictive models Writing Prediction algorithms Data mining robotics and automation

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Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning

Motion Memory: Leveraging Past Experiences to Accelerate Fut...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Dibyendu Das Yuanjie Lu Erion Plaku Xuesu Xiao Department of Computer Science George Mason University VA National Science Foundation Alexandria VA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.

关键词： Memory management Planning robotics and automation Optimization

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DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects

DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynam...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Sun Zhaole Jihong Zhu Robert B. Fisher School of Informatics University of Edinburgh UK School of Physics Engineering and Technology University of York UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deformable linear object (DLO) manipulation is needed in many fields. Previous research on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper manipulation with fixed grasping positions. However, the potential for dexterous manipulation of DLOs using an anthropomorphic hand is under-explored. We present DexDLO, a model-free framework that learns dexterous dynamic manipulation policies for deformable linear objects with a fixed-base dexterous hand in an end-to-end way. By abstracting several common DLO manipulation tasks into goal-conditioned tasks, DexDLO can perform tasks such as DLO grabbing, DLO pulling, DLO end-tip position controlling, etc. Using the Mujoco physics simulator, we demonstrate that our framework can efficiently and effectively learn five different DLO manipulation tasks with the same framework parameters. We further provide a thorough analysis of learned policies, reward functions, and reduced observations for a comprehensive understanding of the framework.

关键词： Deformable models Grasping Task analysis robotics and automation Grippers Manipulator dynamics Physics

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Certifying Bimanual RRT Motion Plans in a Second

Certifying Bimanual RRT Motion Plans in a Second

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Alexandre Amice Peter Werner Russ Tedrake Massachusetts Institute of Technology

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present an efficient method for certifying non-collision for piecewise-polynomial motion plans in algebraic reparametrizations of configuration space. Such motion plans include those generated by popular randomized methods including RRTs and PRMs, as well as those generated by many methods in trajectory optimization. Based on Sums-of-Squares optimization, our method provides exact, rigorous certificates of non-collision; it can never falsely claim that a motion plan containing collisions is collision-free. We demonstrate that our formulation is practical for real world deployment, certifying the safety of a twelve degree of freedom motion plan in just over a second. Moreover, the method is capable of discriminating the safety or lack thereof of two motion plans which differ by only millimeters.

关键词： Safety robotics and automation Trajectory optimization

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Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

Parameter-efficient Prompt Learning for 3D Point Cloud Under...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Hongyu Sun Yongcai Wang Wang Chen Haoran Deng Deying Li Department of Computer Science School of Information Renmin University of China Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on timeconsuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method. Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://***/auniquesun/PPT.

关键词： Point cloud compression Solid modeling Adaptation models Three-dimensional displays Prompt engineering Task analysis robotics and automation

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Time Series Forecasting of KSE-100 Index Using a Hybrid ESN-LSTM Model

Time Series Forecasting of KSE-100 Index Using a Hybrid ESN-...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Taimur Shahzad Gill Syed Ibrahim Zahid New Business Development Asia Petroleum Limited Karachi Pakistan City Health Centre National Health Service Coventry United Kingdom

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

The volatility and complexity of stock markets, particularly in emerging economies like Pakistan, pose significant challenges for accurate forecasting. This paper aims to improve the predictive accuracy of the Karachi Stock Exchange index (KSE-100) by comparing and combining advanced deep learning models. The research investigates the effectiveness of Echo State Networks (ESN), Long Short-Term Memory (LSTM) networks, and a hybrid ESN-LSTM model in capturing both short-term fluctuations and long-term trends. Daily KSE-100 index prices from September 1, 2019, to September 1, 2024, were used, with scaled closing price, 50- and 200-day moving averages as input features. The models' performances were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared and Directional Accuracy (DA). Results indicate that the ESN-LSTM hybrid model outperformed both individual ESN and LSTM models, achieving the lowest MAE of 513.10 and RMSE of 650.59, along with the highest R-squared of 0.975 and DA of 94.12%. The superior performance of the hybrid model is attributed to its ability to use the efficient temporal feature extraction of ESNs and the long-term memory capabilities of LSTMs. These findings suggest that hybrid approaches can significantly improve stock market forecasting accuracy in emerging markets. Future research should explore the integration of sentiment analysis and macroeconomic indicators to further enhance predictive capabilities.

关键词： Sentiment analysis Accuracy Biological system modeling Time series analysis Predictive models Indexes Stock markets Forecasting robotics and automation Long short term memory

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LoRa technology application within the Internet of Flying Things concept for data collection from IoT devices

LoRa technology application within the Internet of Flying Th...

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Alexander Dubelschikov Timur Gamberov Tatyana Tsoy Kuo-Hsien Hsia Evgeni Magid Tikhonov Moscow Institute of Electronics and Mathematics HSE University Moscow Russia Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Graduate School Engineering Science and Technology National Yunlin University of Science & Technology Taiwan The Republic of China

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

The Internet of Flying Things is one of the priority areas for robotics and automation research and applications. A promising case is an application of UAVs in a search and rescue scenario for data collection from pre-positioned IoT devices, which allows assessing and monitoring a current situation in an operation area. This paper presents a concept of using a low-power LoRa protocol to transmit data from smart sensors to UAVs that deliver the data to a dedicated cloud for further processing and analysis. A mock-up of a structure, which is described in the paper, is to be realized using microcontrollers, LoRa modules and smart temperature sensors.

关键词： Temperature sensors Protocols Microcontrollers LoRa Data collection Communications technology Internet of Things robotics and automation Monitoring Intelligent sensors

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TRTM: Template-based Reconstruction and Target-oriented Manipulation of Crumpled Cloths

TRTM: Template-based Reconstruction and Target-oriented Mani...

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IEEE International Conference on robotics and automation (ICRA)

作者： Wenbo Wang Gen Li Miguel Zamora Stelian Coros ETH Zurich Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Precise reconstruction and manipulation of the crumpled cloths is challenging due to the high dimensionality of cloth models, as well as the limited observation at self-occluded regions. We leverage the recent progress in the field of single-view reconstruction to template-based reconstruct the crumpled cloths from their top-view depth observations only, with our proposed sim-real registration protocols. In contrast to previous implicit cloth representations, our reconstruction mesh explicitly describes the positions and visibilities of the entire cloth mesh vertices, enabling more efficient dual-arm and single-arm target-oriented manipulations. Experiments demonstrate that our TRTM system can be applied to daily cloths that have similar topologies as our template mesh, but with different shapes, sizes, patterns, and physical properties. Videos, datasets, pre-trained models, and code can be downloaded from our project website: https://***/TRTM/.

关键词： Protocols Codes Shape Topology robotics and automation Videos

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Jerk-limited Traversal of One-dimensional Paths and its Application to Multi-dimensional Path Tracking

Jerk-limited Traversal of One-dimensional Paths and its Appl...

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IEEE International Conference on robotics and automation (ICRA)

作者： Jonas C. Kiemel Torsten Kröger Institute for Anthropomatics and Robotics – Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present an iterative method to quickly traverse multi-dimensional paths considering jerk constraints. As a first step, we analyze the traversal of each individual path dimension. We derive a range of feasible target accelerations for each intermediate waypoint of a one-dimensional path using a binary search algorithm. Computing a trajectory from waypoint to waypoint leads to the fastest progress on the path when selecting the highest feasible target acceleration. Similarly, it is possible to calculate a trajectory that leads to minimum progress along the path. This insight allows us to control the traversal of a one-dimensional path in such a way that a reference path length of a multi-dimensional path is approximately tracked over time. In order to improve the tracking accuracy, we propose an iterative scheme to adjust the temporal course of the selected reference path length. More precisely, the temporal region causing the largest position deviation is identified and updated at each iteration. In our evaluation, we thoroughly analyze the performance of our method using seven-dimensional reference paths with different path characteristics. We show that our method manages to quickly traverse the reference paths and compare the required traversing time and the resulting path accuracy with other state-of-the-art approaches.

关键词： Accuracy Target tracking Trajectory Iterative methods robotics and automation

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