检索结果-内蒙古大学图书馆

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mario A.V. Saucedo Nikolaos Stathoulopoulos Akash Patel Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object’s inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/BDCMVx2GiQE

关键词： Three-dimensional displays Affordances Computational modeling Semantics Estimation Probability distribution Cognition Planning Indoor environment Optimization

来源：评论

学校读者我要写书评

暂无评论

GN-CNN-LSTM: Financial Market Prediction With Gaussian Noise Embedded CNN LSTM 11

GN-CNN-LSTM: Financial Market Prediction With Gaussian Noise...

引用

11th International Symposium on Telecommunication, IST 2024

作者： Abbasi, Mahsa Kargar, Masoud Ahmadian, Fatemeh Noormohammadzadehmaleki, Deniz Arandan, Amirhossein Hosseini, Nayer Seyed Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Robotics and Soft Technologies Research Center Tabriz Branch Islamic Azad University Tabriz Iran

ISBN: (纸本)9798350356250

Accurate financial market prediction is challenging due to volatility and complex patterns. This study presents a novel approach that injects Gaussian noise into a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model, enhancing prediction accuracy. While denoising techniques such as Autoencoders, Wavelet Transform, Filtering Techniques, and Generative Adversarial Networks (GANs) are commonly used, this research demonstrates that injecting noise during preprocessing significantly improves the performance of the CNNLSTM model. Empirical results using historical data show that the CNN-LSTM model with injected Gaussian noise outperforms traditional models, with notable improvements in MAE and RMSE metrics. Specifically, in SHOMP, SHI, HSI, and XAUUSD, the value of MAE in different sequences improved to 0.017,0.052,0.030, and 0.021, respectively, highlighting the effectiveness of the GN-CNN-LSTM model. These findings underscore the potential of noise-injected deep learning models, instilling optimism for achieving more reliable financial market predictions. © 2024 IEEE.

关键词： Commerce

来源：评论

学校读者我要写书评

暂无评论

Comparison of Corrosion Segmentation Techniques on Oil and Gas Offshore Critical Assets 20

Comparison of Corrosion Segmentation Techniques on Oil and G...

引用

20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Sookpong, Satida Phimsiri, Sasin Tosawadi, Teepakorn Choppradit, Pakcheera Suttichaya, Vasin Utintu, Chaitat Thamwiwatthana, Ek Skyller Solutions Co. Ltd. Bangkok Thailand Ai and Robotics Ventures Co. Ltd. Bangkok Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand

ISBN: (纸本)9798350310467

Corrosion is one of the biggest problems that can lead to fatal disasters in the industry. Investigate corrosion and perform timely maintenance on the asset to prevent corrosion issues. However, the investigation of the inspector onsite can lead to a time-consuming and dangerous problem. For that reason, corrosion detection from offshore asset images is necessary. This paper proposes the implementation of a segmentation technique for automatically detecting corrosion damage on oil and gas offshore critical assets. We compare three semantic segmentation architectures, namely UNET, PSPNet, and vision transformer. The image data was collected by unmanned aerial vehicles (UAV). The experiment also compared the full-image dataset and sliced-image dataset with 512 × 512 pixels of the image. The results are calculated using the F1 score and IoU score of the predicted and annotated mask. The experiment shows that ViT-Adapter trained with a full-image dataset receives the best IoU score and F1 score, which are 0.8964 and 0.9451, respectively. However, the specialist inspector prefers the result from the slicing experiment since the slicing prediction offers a more precise corrosion mask. © 2023 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

TFMarker: A Tangible Fiducial Pattern for Enabling Camera-assisted Guided Landing in SubT Environments

TFMarker: A Tangible Fiducial Pattern for Enabling Camera-as...

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Mario A. V. Saucedo Akash Patel Niklas Dahlquist Yifan Bai Björn Lindqvist Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Visual servoing plays a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to aerial manipulation. This article proposes TFMarker, a novel tangible fiducial pattern for enabling camera-assisted guided landing of UAVs by using the visual features from color markers as the main source of information. TFMarker is structured around a 4-point fiducial marker, allowing for accurate, precise, and consistent pose estimation in different environments and lighting conditions, while also offering resilience to motion blur. The presented detection framework is based on a three-step architecture, where the first step uses Gaussian and color filtering in addition to morphological operation in order to generate a robust detection of the markers. The second step uses the Gift Wrapping Algorithm, to organize the same-color markers based on their relative positioning with respect to the off-color marker. Finally, the Perspective-n-Point optimization problem is solved in order to extract the pose (i.e. position and orientation) of the proposed pattern with respect to the vision sensor. The efficacy of the proposed scheme has been extensively validated in indoor and SubT environments for the task of autonomous landing using a custom-made UAV. The experimental results showcase the performance of the proposed method, which presents a better detection rate in both environments while retaining similar accuracy and precision to the baseline approach. For the video of the experimental evaluation please refer to the following link: https://***/Zh13OObp15Q

关键词： Visualization Accuracy Filtering Color Vision sensors Autonomous aerial vehicles Visual servoing Wrapping Optimization Morphological operations

来源：评论

学校读者我要写书评

暂无评论

Body-aware Local Navigation for Asymmetric Holonomic Robots using Control Barrier Functions

Body-aware Local Navigation for Asymmetric Holonomic Robots ...

引用

European Control Conference (ECC)

作者： Akshit Saradagi Scott Fredriksson Anton Koval George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this article, we propose a body-aware local navigation strategy for asymmetric holonomic robots for collision-free navigation in narrow pathways with sharp turns. In such scenarios, a robot with non-circular or asymmetric footprint that is comparable to the dimension of the pathways collides with walls when tracking Voronoi paths or risk-aware paths. This problem is addressed in this article through a novel multi-control barrier functions (CBF) based control strategy that achieves the objective of safe collision-free maneuvering at sharp turns. The proposed method is significantly computationally light in comparison to approaches based on model predictive control and online occupancy-grid based free-space and collision detection. In the proposed approach, a minimal set of parameters that characterize a sharp turn and the robot footprint are used to define six control barrier functions that define safe and unsafe regions of operation for a robot. A quadratic programming based CBF safety filter is designed that takes a nominal goal-reaching control as input and returns a minimally-deviating output that enforces the control barrier constraints and renders the safe set forward invariant throughout the turning maneuver. The three kinematic control inputs of the holonomic robot are shared in a conflict-free manner among the six control barrier constraints. The proposed local navigation approach was thoroughly validated in multiple scenarios in a simulated environment, where a robot with asymmetric footprint achieves collision-free maneuvering along multiple sharp turns, while respecting the safety and actuation constraints.

关键词： Laser radar Navigation Kinematics Turning Safety Quadratic programming Mobile robots

来源：评论

学校读者我要写书评

暂无评论

STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

STAGE: Scalable and Traversability-Aware Graph based Explora...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Akash Patel Mario A. V. Saucedo Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

关键词： Legged locomotion Uncertainty Laser radar Navigation Robot vision systems Memory management Cameras

来源：评论

学校读者我要写书评

暂无评论

D-MARL: A Dynamic Communication-Based Action Space Enhancement for Multi Agent Reinforcement Learning Exploration of Large Scale Unknown Environments

D-MARL: A Dynamic Communication-Based Action Space Enhanceme...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gabriele Calzolari Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. In general, communication between autonomous systems is crucial when exploring large and unstructured environments. In such real-world scenarios, data transmission is limited and relies heavily on inter-agent proximity and the attributes of the autonomous platforms. In the proposed approach, each agent’s policy is optimized by utilizing the heterogeneous-agent proximal policy optimization algorithm to autonomously choose whether to communicate or explore the environment. To accomplish this, multiple novel reward functions are formulated by integrating inter-agent communication and exploration. The investigated approach aims to increase efficiency and robustness in the mapping process, minimize exploration overlap, and prevent agent collisions. The D-MARL policies trained on different reward functions have been compared to understand the effect of different reward terms on the collaborative attitude of the homogeneous agents. Finally, multiple simulation results are provided to prove the efficacy of the proposed scheme.

关键词： Autonomous systems Simulation Merging Employment Collaboration Reinforcement learning Robustness Data communication Optimization Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers

arXiv

引用

arXiv 2024年

作者： Seisa, Achilleas Santi Satpute, Sumeet Gajanan Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems. © 2024, CC BY.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Robotic Exploration through Semantic Topometric Mapping

Robotic Exploration through Semantic Topometric Mapping

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Scott Fredriksson Akshit Saradagi George Nikolakopoulos Department of Computer Science Robotics and AI Group Electrical and Space Engineering at Luleå University of Technology Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method’s online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time.

关键词： Measurement Laser radar Navigation Semantics Skeleton Iron Indoor environment

来源：评论

学校读者我要写书评

暂无评论

Job Shop Scheduling Using Genetic and Heuristic Exchange Algorithms for AGVs

引用

Journal of Institute of Control, robotics and Systems 2022年第2期28卷 191-201页

作者： Wang, Jun-Kyung Eoh, Gyuho Park, Tae-Hyoung Department of Control Robot Engineering Chungbuk National University Korea Republic of Industrial AI Research Center Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Solving a job shop scheduling problem (JSSP) entails allocating entire jobs to machines to minimize the processing time. Generally, the transit time between machines is not considered in the conventional JSSP because it is shorter than the processing time. In a real production environment, however, the transit time of an autonomous ground vehicle (AGV) is long due to the need to solve random problems, such as finding an indirect route or avoiding collisions. Moreover, AGVs sometimes need to transport the job on directional roads with upstream and downstream constraints. Therefore, we present an AGV job scheduling algorithm considering the transit time. This study considers not only the transportation times of the jobs from one machine to another but also collisions on limited roads, similar to in a real environment. A near-optimal job sequence was obtained by combining the genetic algorithm (GA) with simulation and was comparing this with the traditional method. Additionally, this paper presents heuristics that deal with deadlocks in the process of combining JSSP and simulation. The combination of GA and heuristics was tested using various simulations. © ICROS 2022.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：