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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341-350 订阅

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Towards Automatic Inference of Behavioral Component models for ROS-Based robotics Systems 24

Towards Automatic Inference of Behavioral Component Models f...

引用

44th international conference on Software Engineering: Companion Proceedings

作者： Durschmid, Tobias Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9798400705021

Model-based analysis is a common technique to identify incorrect behavioral composition of complex, safety-critical systems, such as robotics systems. However, creating structural and behavioral models for hundreds of software components manually is often a labor-intensive and error-prone process. I propose an approach to infer behavioral models for components of systems based on the Robot Operating System (ROS), the most popular framework for robotics systems, using a combination of static and dynamic analysis by exploiting assumptions about the usage of the ROS framework. This work is a contribution towards making well-proven and powerful but infrequently used methods of model-based analysis more accessible and economical in practice to make robotics systems more reliable and safer.

关键词： Robot Operating System

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Attention-Enhanced Lightweight Hourglass Network for Human Pose Estimation

Attention-Enhanced Lightweight Hourglass Network for Human P...

引用

2024 Australasian conference on robotics and automation, ACRA 2024

作者： Kappan, Marsha Mariya Sandoval, Eduardo Benitez Meijering, Erik Cruz, Francisco School of Computer Science and Engineering University of New South Wales Sydney Australia School of Art and Design Creative Robotics Lab. University of New South Wales Sydney Australia Escuela de Ingeniería Universidad Central de Chile Santiago Chile

Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However, most of the existing methods are computationally expensive or have complex architecture. Here we propose a lightweight attention based pose estimation network that utilizes depthwise separable convolution and Convolutional Block Attention Module on an hourglass backbone. The network significantly reduces the computational complexity (floating point operations) and the model size (number of parameters) containing only about 10% of parameters of original eight stack Hourglass network. Experiments were conducted on COCO and MPII datasets using a two stack hourglass backbone. The results showed that our model performs well in comparison to six other lightweight pose estimation models with an average precision of 72.07. The model achieves this performance with only 2.3M parameters and 3.7G FLOPs. © 2024 Australasian robotics and automation Association. All rights reserved.

关键词： Convolution

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Decentralized Data Collection via Swarm Contracts: Study on Crowd-Sourced Google Maps 9

Decentralized Data Collection via Swarm Contracts: Study on ...

引用

9th international conference on Control, automation and robotics (ICCAR)

作者： Mallikarachchi, Sanjaya Ho, Bonnie Kanj, Iyad Seneviratne, Oshani Godage, Isuru S. DePaul Univ Sch Comp Jarvis Coll Comp & Digital Media Chicago IL 60614 USA Rensselaer Polytech Inst Dept Comp Sci Troy NY USA Texas A&M Univ Dept Eng Tech & Ind Distribut College Stn TX USA Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX USA

ISBN: (纸本)9798350322514

"Data is the new oil" has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View((R)) map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View((R)) Publish API, which allows for the contribution of street view data to Google Maps((R)) to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View((R)) Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View((R)) maps.

关键词： Cyber-Physical Systems Swarm robotics Smart Contracts Blockchain-based crowd-sourcing

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CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

CurveFormer: 3D Lane Detection by Curve Propagation with Cur...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Bai, Yifeng Chen, Zhirong Fu, Zhangjie Peng, Lang Liang, Pengpeng Cheng, Erkang Chinese Acad Sci Inst Intelligent Machines HFIPS Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China NullMax Shanghai 201210 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

ISBN: (纸本)9798350323658

3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.

关键词： Iterative methods

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MotionGS : Compact Gaussian Splatting SLAM by Motion Filter 7

MotionGS : Compact Gaussian Splatting SLAM by Motion Filter

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7th international conference on robotics, Control and automation Engineering, RCAE 2024

作者： Guo, Xinli Zhang, Weidong Liu, Ruonan Han, Peng Chen, Hongtian Shanghai Jiao Tong University Department of Automation Shang Hai China Tianjin University Department of Automation Tian Jin China

ISBN: (纸本)9798350355642

With their high-fidelity scene representation capability, the attention of SLAM field is deeply attracted by the Neural Radiation Field (NeRF) and 3D Gaussian Splatting (3DGS). Recently, there has been a surge in NeRF-based SLAM, while 3DGS-based SLAM is sparse. A novel 3DGS-based SLAM approach with a fusion of deep visual feature, dual keyframe selection and 3DGS is presented in this paper. Compared with the existing methods, the proposed tracking is achieved by feature extraction and motion filter on each frame. The joint optimization of poses and 3D Gaussians runs through the entire mapping process. Additionally, the coarse-to-fine pose estimation and compact Gaussian scene representation are implemented by dual keyframe selection and novel loss functions. Experimental results demonstrate that the proposed algorithm not only outperforms the existing methods in tracking and mapping, but also has less memory usage. © 2024 IEEE.

关键词： Mapping

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Learning Interactive Driving Policies via Data-driven Simulation 39

Learning Interactive Driving Policies via Data-driven Simula...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wang, Tsun-Hsuan Amini, Alexander Schwarting, Wilko Gilitschenski, Igor Karaman, Sertac Rus, Daniela MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Univ Toronto Dept Comp Sci Toronto ON Canada Toyota Res Inst TRI Toyota Japan

ISBN: (纸本)9781728196817

Data-driven simulators promise high dataefficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a data-driven simulation engineA that uses inpainted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multiagent interactions and allows for training via state-of-theart policy learning methods. We evaluate the approach for learning standard interaction scenarios in driving. In extensive experiments, our work demonstrates that the resulting policies can be directly transferred to a full-scale autonomous vehicle without making use of any traditional sim-to-real transfer techniques such as domain randomization.

关键词： Training Learning systems Analytical models automation Data models Complexity theory Task analysis

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2023 8th international conference on robotics and automation Engineering Quantifying the Performance of Deep Neural Networks in Predicting Curvature and Force Output Response of a Pneumatic Soft Actuator 8

2023 8th International Conference on Robotics and Automation...

引用

8th international conference on robotics and automation Engineering, ICRAE 2023

作者： Livinus, Ememobong N. Giannaccini, Maria Elena Starkey, Andrew Aphale, Sumeet S. Aberdeen United Kingdom

ISBN: (纸本)9798350327656

Soft robotics promises new opportunities for solving problems that were limited by rigid robots due to their compliant physical structure. Pneumatic Soft Actuators (PSA) are a class of soft robots that have gained popularity due to their cost-effective manufacturing and high force-generating capability. Due to their highly nonlinear dynamics, accurate prediction of their response to input pressure (curvature) as well as force output is challenging. Artificial Neural Networks were employed to this effect. This work shows a comparative analysis of three variants of the recurrent neural network used in this paper for predicting the behavior of soft robotic actuators. Each network was trained using the same data set to predict the position, curvature, and applied force by the soft robotics on an external force. The result shows the different performance of the three models, evaluating them using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The Long Short-term Memory (LSTM) model outperformed both the Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN) models, producing the lowest error in the three metrics. © 2023 IEEE.

关键词： Forecasting

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Conditioned Human Trajectory Prediction using Iterative Attention Blocks 39

Conditioned Human Trajectory Prediction using Iterative Atte...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Postnikov, Aleksey Gamayunov, Aleksander Ferrer, Gonzalo Sberbank Robot Lab Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781728196817

Human motion prediction is key to understand social environments, with direct applications in robotics, surveillance, etc. We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians' positions prediction in urban-like environments conditioned by the environment: map and surround agents. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion, allowing to capture the important features in the environment that improve prediction. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models, which makes our approach easily extendable and configurable, depending on the data available. We report results performing similarly with SoTA models on publicly available and extensible-used datasets with uni-modal prediction metrics ADE and FDE.

关键词： Measurement Predictive models Multilayer perceptrons Transformers Prediction algorithms Data models Iterative algorithms

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Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation 39

Symphony: Learning Realistic and Diverse Agents for Autonomo...

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IEEE international conference on robotics and automation (ICRA)

作者： Igl, Maximilian Kim, Daewoo Kuefler, Alex Mougin, Paul Shah, Punit Shiarlis, Kyriacos Anguelov, Dragomir Palatucci, Mark White, Brandyn Whiteson, Shimon Waymo Res Mountain View CA 94043 USA

ISBN: (纸本)9781728196817

Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on the road. However, existing LfD methods are typically insufficient, yielding policies that frequently collide or drive off the road. To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. However, it can also harm diversity, i.e., how well the agents cover the entire distribution of realistic behaviour, as pruning can encourage mode collapse. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. The use of such goals ensures that agent diversity neither disappears during adversarial training nor is pruned away by the beam search. Experiments on both proprietary and open Waymo datasets confirm that Symphony agents learn more realistic and diverse behaviour than several baselines.

关键词： Training automation Roads Diversity reception Search problems Trajectory Automobiles

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Two Points Classical Identification methods on the Example of a Three-Tank Interacting and Non-Interacting System 26

Two Points Classical Identification Methods on the Example o...

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26th international conference on methods and models in automation and robotics

作者： Czemplik, Anna Jagodzinski, Jacek Wroclaw Univ Sci & Technol Dept Control Syst & Mechatron Wroclaw Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

This paper mainly focuses on various two-point methods of identifying the First Order Plus Time Delay (FOPTD) model, and their impact on control quality. The following criteria were used to match the identification method: ISE between the response of the real system and the model and the normalized delay time. The work used two different tuning techniques: QDR and pidtune. A three-tank interacting and non-interacting system was used as a model for the tests. All tests were simulated in Matlab environment, ISE and settling time were used to assess the quality of control.

关键词： First Order process Plus Time Delay (FOPTD) two-point methods identification interacting and non-interacting system

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