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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

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Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model

Efficient Heuristic Generation for Robot Path Planning with ...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Li, Zhaoting Wang, Jiankun Meng, Max Q-H Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781728190778

Robot path planning is difficult to solve due to the contradiction between the optimality of results and the complexity of algorithms, even in 2D environments. To find an optimal path, the algorithm needs to search all the state space, which costs many computation resources. To address this issue, we present a novel recurrent generative model (RGM), which generates efficient heuristic to reduce the search efforts of path planning algorithms. This RGM model adopts the framework of general generative adversarial networks (GAN), which consists of a novel generator that can generate heuristic by refining the outputs recurrently and two discriminators that check the connectivity and safety properties of heuristic. We test the proposed RGM module in various 2D environments to demonstrate its effectiveness and efficiency. The results show that, compared with a model without recurrence, the RGM successfully generates appropriate heuristic in both seen and new unseen maps with higher accuracy, demonstrating the good generalization ability of the RGM model. We also compare the rapidly-exploring random tree star (RRT*) with generated heuristic and the conventional RRT* in four different maps, showing that the generated heuristic can guide the algorithm to efficiently find both initial and optimal solutions in a faster and more efficient way.

关键词： Costs Heuristic algorithms computational modeling Conferences Refining Generative adversarial networks Path planning

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3D Feedback Motion Planning Of Unmanned Underwater Vehicles Using Sparsity Enhanced Random Sequential Composition*

3D Feedback Motion Planning Of Unmanned Underwater Vehicles ...

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IEEE international Conference on automation Science and Engineering (CASE)

作者： Emre Ege Eminalp Koyuncu M. Mert Ankarali Maren Robotics Ankara Türkiye Department of Electrical and Electronics Engineering Center for Robotics and Artificial Intelligence Middle East Technical University Ankara Türkiye

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, a new, robust, and computationally feasible motion planning algorithm is introduced to solve the path planning, navigation, and probabilistic coverage problems related to the Unmanned Underwater Vehicles (UUV). The approach given in this paper is based on feedback motion planning with random sequential composition (RSC) method. Initially, the RSC method was capable of 2D motion planning with a sparse tree structure. In this work, the sparsity of the RSC method is enhanced using an approach called "circular enlarged funnels," and the algorithm is extended for 3D path planning of UUVs. Then, the proposed method is tested on MATLAB software in a 2D environment with the kinematic model of the UUV. After that, the 3D extension is tested with the realistic dynamical model of the UUV built on ROS/Gazebo environment, with and without environmental disturbances, to show the robustness of the resulting feedback policies. The results of these simulations showed that the proposed method can solve the path planning problem of UUVs in a robust and computationally feasible way with enhanced sparsity.

关键词： Solid modeling Autonomous underwater vehicles Three-dimensional displays Simulation computational modeling Kinematics Probabilistic logic Mathematical models Planning Vehicle dynamics

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Application of an Improved Residual Attention Neural Network in Mechanical Part Classification 9th

Application of an Improved Residual Attention Neural Network...

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9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Zhang, Tianrui Wang, Aizeng School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819629138

This paper introduces an improved neural network that integrates residual network and attention mechanisms, achieving a significant improvement of 99.50% accuracy in mechanical part classification tasks. The proposed network enhance accuracy, precision, recall, and F1-score compared to traditional CNNs and other advanced networks in handling complex backgrounds and multi-angle part images. This advancement shows potential in smart manufacturing, offering greater automation, minimizing human intervention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart manufacturing

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Local Gaussian Modifiers (LGMs): UAV Dynamic Trajectory Generation for Onboard Computation

Local Gaussian Modifiers (LGMs): UAV Dynamic Trajectory Gene...

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international Conference on Unmanned Aircraft Systems (ICUAS)

作者： Miguel Fernandez-Cortizas David Perez-Saura Rafael Perez-Segui Javier Rodriguez-Vazquez Juan S. Cely Pascual Campoy Computer Vision and Aerial Robotics group (CVAR) Centre for Automation and Robotics (CAR) Universidad Politécnica de Madrid (UPM) Madrid Spain Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid Spain Intelligent Robotics Lab Rey Juan Carlos University Madrid Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Agile autonomous drones are becoming increasingly popular in research due to the challenges they represent in fields like control, state estimation, or perception at high speeds. When all algorithms are computed onboard the UAV, computational limitations make the task of agile flight even more difficult. One of the most computationally expensive tasks in agile flight is the generation of optimal trajectories. When these trajectories must be updated online due to changes in the environment or uncertainties, this high computational cost may result in insufficient time to reach the desired waypoints, which could cause a drone crash in cluttered environments. In this paper, we present Local Gaussian Modifiers (LGMs), a fast and lightweight way of modifying computationally heavy trajectories when recalculating them in time is not possible due to computational limitations. Moreover, we propose a strategy for deciding when is convenient to use these modifiers or recalculate the whole trajectory based on an estimation of the computational time of this trajectory generation. A trajectory blending procedure is also proposed to ensure smoothness in UAV control when a new trajectory is computed. Our approach was validated in simulation, being able to pass through a race circuit with moving gates, achieving speeds up to 16.0 m/s. Real flight validation was also performed achieving speeds up to 4.0 m/s in a fully autonomous pipeline using onboard computing.

关键词： Uncertainty Pipelines Logic gates Autonomous aerial vehicles Robustness Motion capture Trajectory

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Comparison of optimization algorithms for installation error calibration of accelerometer arrays

Comparison of optimization algorithms for installation error...

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international symposium on computational intelligence and Informatics

作者： Richard Pesti Peter Sarcevic Dominik Csík Istvan Nagy Akos Odry Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary Bánki Donát Faculty of Mechanical and Safety Engineering Óbuda University Budapest Hungary

Accelerometers play a crucial role in localization problems, but their accuracy depends on the precision of the calibration process. In this paper, a comparison of optimization algorithms is presented within the scope of between sensor error calibration. The Particle Swarm optimization (PSO) and Genetic Algorithm (GA) are proposed for the problem, and tested in three different setups. Moreover, the MATLAB built-in fmincon minimization algorithm is also evaluated for calibration parameter estimation. A custom fitness function is proposed to obtain the between sensor misalignment and bias errors, for two accelerometers. The analysis highlights that the algorithms can successfully determine the between sensor error parameters. The conducted comparison shows that fmincon reached an outstanding speed in terms of convergency, but in overall PSO has produced better results compared to GA and fmincon. PSO is more suitable for the calibration problem because of its robustness and stability characteristics.

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Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly

Online-Adaptive Anomaly Detection for Defect Identification ...

引用

IEEE international Conference on automation Science and Engineering (CASE)

作者： Siddhant Shete Dennis Mronga Ankita Jadhav Frank Kirchner Robotics Innovation Center of German Research Center for Artificial Intelligence GmbH (DFKI) Bremen Germany Robotics Group of the University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Anomaly detection deals with detecting deviations from established patterns within data. It has various applications like autonomous driving, predictive maintenance, and medical diagnosis. To improve anomaly detection accuracy, transfer learning can be applied to large, pre-trained models and adapt them to the specific application context. In this paper, we propose a novel framework for online-adaptive anomaly detection using transfer learning. The approach adapts to different environments by selecting visually similar training images and online fitting a normality model to EfficientNet features extracted from the training subset. Anomaly detection is then performed by computing the Mahalanobis distance between the normality model and the test image features. Different similarity measures (SIFT/FLANN, Cosine) and normality models (MVG, OCSVM) are employed and compared with each other. We evaluate the approach on different anomaly detection benchmarks and data collected in controlled laboratory settings. Experimental results showcase a detection accuracy exceeding 0.975, outperforming the state-of-the-art ET-NET approach.

关键词： Training Adaptation models Accuracy computational modeling Transfer learning Benchmark testing Feature extraction Aircraft manufacture Aircraft Anomaly detection

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Utilizing Transfer Learning in In Vitro Dissolution Prediction Based on Raman Maps

Utilizing Transfer Learning in In Vitro Dissolution Predicti...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Gábor Knyihár Kristóf Csorba Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

In vitro dissolution prediction is a field of phar-maceutical research that aims to predict a tablet's dissolution profile using non-destructive methods based on the tablet's measured manufacturing parameters or composition. One of the most popular non-destructive measurements is Raman chemical imaging, which outputs a Raman map. However, because of the current development of the Raman imaging technique, obtaining large training datasets is very expensive and time-consuming. Still, training popular complex deep learning architectures that may achieve better accuracy is only possible with a large dataset. Therefore this article focuses on utilising pre-trained networks and the transfer learning technique to attempt to solve the problem of the small size of training samples. The results showed that deep neural networks with data augmentation and transfer learning could achieve better accuracy than simple convolutional neural networks. The presented strategies showed the need for an extensive dataset could be handled, and the accuracy could be enhanced, although there is room for further refinement.

关键词： Training Accuracy Uncertainty Current measurement Transfer learning Imaging Manufacturing

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A Review of DNN and GPU in Optical Proximity Correction

A Review of DNN and GPU in Optical Proximity Correction

引用

Electronics Design automation (ISEDA), international symposium of

作者： Huming Zhu Xiangyu Jiang Delong Shu Xinyue Cheng Biao Hou Hailong You Key Laboratory of Intelligent Perception and Image Understanding School of Artificial Intelligence Xidian University Xi'an China Xidian University Xi'an China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

Optical Proximity Correction (OPC) is a resolution enhancement technique. It compensates for imaging distortions by modifying the mask patterns. In advanced nodes, inverse lithography technology (ILT) is used to produce more complex and finer mask shapes. However, ILT increases the computational complexity and runtime. In recent years, researchers have attempted to accelerate the process using GPUs and have improved the accuracy using deep neural networks (DNN). In the past decade, new breakthroughs in various DNN fields, including but not limited to CNN, GNN, GAN, Transformer and other algorithms, have further improved the accuracy of OPC algorithms. The high performance of GPU has provided a foundation for OPC and DNN. In this paper, we summarize the key OPC technologies during this period and make projections for them in the future.

关键词： Accuracy Optical design Lithography Graphics processing units Artificial neural networks Optical distortion Optical imaging

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Indoor Mapping and Analysis Based on Optimized Existing 2D SLAM Algorithm

Indoor Mapping and Analysis Based on Optimized Existing 2D S...

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robotics, Artificial intelligence and Intelligent Control (RAIIC), international Conference on

作者： Yuntao Xiao Guowei Zhang Jinwen Hu Youtao Zhou Haoyu Fan Shimian Zhang The School of Automation Engineering University of Electric Power Shanghai China Shanghai HRSTEK Co. Ltd Shanghai China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

During the process of enabling autonomous movement of robots in unknown environments, deficiencies in existing algorithms were observed in practical applications. In order to reduce computational resource usage and improve the smoothness of robot autonomous navigation, it was necessary to optimize existing 2D laser mapping algorithms. After researching existing 2D laser mapping algorithms, the Cartographer algorithm was selected for optimization. Optimizing the local SLAM process in Cartographer addressed issues such as map sliding and unclear map details during sub-map construction. Optimizing the global SLAM process improved map consistency and global localization accuracy, as well as enhanced loop closure detection. For processors with lower performance, delay optimization was adopted to improve mapping efficiency and reduce redundant sub-maps. Experimental results indicate that the optimized algorithm resolves issues related to insufficient hardware configuration, improves handling of motion distortions and map drift, enhances mapping efficiency in complex environments, and provides clearer description of detailed environmental features. This optimization method offers an improved approach for indoor mapping using the Cartographer 2D SLAM algorithm.

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Concept and Challenges in Microcontroller Artificial intelligence Development

Concept and Challenges in Microcontroller Artificial Intelli...

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international symposium for Design and Technology of Electronics Packages (SIITME)

作者： Cosmina Corches Adelina Ioana Ilieş Mihai Daraban Gabriel Chindris Automation Department Technical University of Cluj-Napoca Cluj-Napoca Romania Applied Electronics Department Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

There is a significant lag between the year a great idea is first proposed and the year it is eventually adopted and mainstreamed. The foundations of the Internet of Things (IoT) were introduced between 1960 and 1980, the concept was introduced in 1990, and its growth and expansion came after 2000. Proliferation and discussions on standardization were proposed after another 10 years. IoT evolution and development are tightly coupled to cloud-fog-edge architectures with the assumption that a significant amount of advanced processing occurs not on IoT devices but on clouds and fog. However, as the technology evolved, the required amount of data and connections indicated that previous ideas and concepts were not realistic and that data processing at the edge layer was required.

关键词： Training Cloud computing Privacy Microcontrollers computational modeling Virtual environments Standardization Internet of Things Artificial intelligence Synthetic data

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