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Neural Potential Field for Obstacle-Aware Local Motion Planning

Neural Potential Field for Obstacle-Aware Local Motion Plann...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Muhammad Alhaddad Konstantin Mironov Aleksey Staroverov Aleksandr Panov Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny Russia Artificial Intelligence Research Institute Moscow Russia Federal Research Center "Computer Science and Control" Moscow Russia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles.

关键词： Costs Trajectory planning computational modeling Computer architecture Predictive models Vectors Planning

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Biologically-inspired visual landmark learning and navigation for mobile robots

Biologically-inspired visual landmark learning and navigatio...

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ieee international Workshop on Intelligent Robots and Systems (IROS)

作者： G. Bianco A. Zelinsky Department of Electronics for Automation University of Brescia Brescia Italy Department of Systems Engineering RSISE Australian National University Canberra ACT Australia

Presents a biologically-inspired method for navigating using visual landmarks which have been self-selected within natural environments. A landmark is a region of the grabbed image which is chosen according to its reliability measured through a phase (turn back and look) that mimics the behavior of some social insects. From the self-chosen landmarks suitable navigation information can be extracted following a well known model introduced in biology to explain the bee's navigation behavior. The landmark selection phase affects the conservativeness of the navigation vector field thus allowing us to explain the navigation model in terms of a visual potential function which drives the navigation to the goal. The experiments have been performed using a Nomad200 mobile robot equipped with monocular color vision.

关键词： Navigation Mobile robots Biological system modeling robotics and automation Insects Data mining Animals Robot sensing systems Phase measurement computational biology

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An Intelligent Architecture for Integrated Control and Asset Management for Industrial Processes

An Intelligent Architecture for Integrated Control and Asset...

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ieee international symposium on Intelligent Control (ISIC)

作者： J.H. Taylor A.F. Sayda Department of Electrical & Computer Engineering University of New Brunswick Fredericton NB Canada

Abnormal event management (AEM) in large manufacturing plants has evolved as a higher and increasingly vital function of process control. In this paper, an intelligent information management and control system is introduced. The different computational agents (i.e., modules) of the system are embodied in a three-layered cognitive hierarchy, which offers intelligent behavior at the system level, as well as at the level of specialized task agents. At the lower level, agents generate goal-seeking reactive behavior. Three different fault detection and isolation agents (i.e., three complementary techniques) are embedded to generate three different assessments and to enhance the fault isolation process. Other utility agents are also incorporated to address topics such as process model identification and optimization. At the middle layer, agents enable decision making, planning, and deliberative behavior. Two case-based reasoning agents are incorporated, the first manages the system in normal operation, while the other handles faulty process situations. A meta-management agent at the highest level monitors and coordinates other agents so as to make the whole system performance more robust and coherent

关键词： Centralized control Asset management Industrial control computational intelligence Computer architecture Electrical equipment industry Manufacturing industries Manufacturing processes Process control Information management

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Cognitive Architectures and their Applications

Cognitive Architectures and their Applications

引用

international Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Christian Lebiere Edward A. Cranford Michael Martin Donald Morrison Andrea Stocco Psychology Department Carnegie Mellon Univ Pittsburgh PA Psychology Department Univ. of Washington Seattle WA

Cognitive architectures are computational implementations of unified theories of cognition. The consensus of 50 years of research in cognitive architectures can be captured in the form of a Common Model of Cognition that can provide a guide for applications in neuroscience, artificial intelligence and robotics. Being able to represent human cognition in computational form enables a wide range of applications when humans and machines interact. Using cognitive models to represent common ground between deep learners and human users enables adaptive explanations. Cognitive models representing the behavior of cyber attackers can be used to optimize cyber defenses including techniques such as deceptive signaling. Cognitive models of human-automation interaction can improve robustness of human-machine teams by predicting disruptions to measures of trust under various adversarial situations.

关键词： Adaptation models Neuroscience computational modeling Computer architecture Predictive models Cognition Robustness

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Distributed computing framework of intelligent sensor network for electric power internet of things

Distributed computing framework of intelligent sensor networ...

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ieee Joint international Information Technology and Artificial intelligence Conference (ITAIC)

作者： Dawei Li Ruimo Yu Chunhe Song Shu Li Gengtao Jia Xiaoli Zhou Start Grid Shanghai Information & Telecommunication Company Shanghai China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

The power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. This paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of Things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data.

关键词： Training Analytical models computational modeling Data aggregation Data models Internet of Things Intelligent sensors

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Vehicle license plate character recognition by neural networks

Vehicle license plate character recognition by neural networ...

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ieee international symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： T. Nukano M. Fukumi M. Khalid University of Tokushima Tokushima Japan Centre for Artificial Intelligence and Robotics (CAIRO) Universiti Teknologi Malaysia Kuala Lumpur Malaysia

This paper tries to recognize characters of vehicle license plates in Malaysia. Vehicle license plate recognition is one of important techniques that can be used for the identification of vehicles. It is useful in many applications such as entrance admission, security, parking control, airport or harbor cargo control, road traffic control, speed control, toll gate automation and so on. In this paper, when part of character and background are dissociated, the threshold of digitalization is important and is determined using a neural network with a three-layered structure. Furthermore, in the extracted character portions, we recognize them by obtaining the features of directional line-elements. Computer simulations show that character recognition can be effectively carried out using such a method.

关键词： Vehicles Licenses Character recognition Neural networks Automatic control Airports Roads Traffic control Velocity control automation

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A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses

A Model Integrating Wavelet Convolution and Linear Attention...

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Information Technology,Big Data and Artificial intelligence (ICIBA), international Conference on ieee international Conference on

作者： Tao Li Zhengli Zhang Ligang Li Guozheng Du State Grid Shandong Electric Power Company Material Company Jinan China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Accurate inventory management in power warehouses is crucial for maintaining consistency between records and actual stock levels. However, the complex storage layout and the diversity of stored materials often lead to human errors during manual inventory checks, resulting in inefficiencies. To address these challenges, we collected 2,000 images from power warehouse environments and created a binary classification dataset containing images of correctly and incorrectly placed items. We propose a novel network, CWAMNet, specifically designed for visual inventory management in complex power warehouse environments. CWAMNet integrates wavelet convolution and a linear attention mechanism to overcome the limitations of traditional convolutional neural networks (CNNs) and standard attention mechanisms, achieving a balance between model capacity and computational efficiency.

关键词： Solid modeling Visualization Attention mechanisms Accuracy Convolution computational modeling Inventory management computational efficiency Convolutional neural networks Standards

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Grasp Quality Evaluation Network for Surface-to-Surface Contacts in Point Clouds

Grasp Quality Evaluation Network for Surface-to-Surface Cont...

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ieee international Conference on automation Science and Engineering (CASE)

作者： Jian Ruan Houde Liu Anshun Xue Xueqian Wang Bin Liang Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Automation Tsinghua University Beijing China Research Institute of Tsinghua University in Shenzhen Shenzhen China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

The problem of grasping is essential in robotics, in which robotic hands grasp target objects by deploying contact force. The contact between robotic hand and target object is always reduced to point-to-point while generally the actual type should be surface-to-surface. In this paper, we propose a novel surface contact model to parameterize the contact area. It computes the grasp quality of the contacts between the robotic hand and target object, then determines whether or not the surface-contact-based end effectors can resist an external wrench on target object. The key idea of the proposed approach is that the contact modeling is based on surface-to-surface contact rather than equivalent contact points which is more in line with the actual situations. Then we propose a grasp quality evaluation network based on surface-to-surface contact to evaluate grasp motion, which can capture the geometric feature of the actual contact area and classify the quality level of input grasp. Experimental results and comparisons with state-of-the-art methods (e.g. GPD and PointNetGPD) demonstrate that our approach could achieve superior performance.

关键词： Three-dimensional displays Grasping Robot sensing systems Detectors Grippers computational modeling

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CCFD-SSL: Optimizing Real-Time Credit Card Fraud Detection Using Self-Supervised Learning and Contrastive Representations

CCFD-SSL: Optimizing Real-Time Credit Card Fraud Detection U...

引用

robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 ieee international Conference on

作者： Md Al Rafi Gourab Nicholas Rodrigues Md Nazmul Hossain Mir M D Shahriar Mahmud Bhuiyan Arifa Akter Eva Aminun Nahar Kamruddin Nur Washington University of Science and Technology Alexandria USA American International University - Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Credit card fraud poses an ongoing challenge for financial entities, especially within real-time transaction systems. This paper presents an innovative application of Self-Supervised Learning (SSL) aimed at tackling the difficulties associated with highly skewed credit card fraud detection (CCFD) datasets. Using transaction data from European cardholders, our SSL-based method improves the model’s ability to differentiate between fraudulent and legitimate transactions, with minimal reliance on extensive labeled datasets. The approach utilizes contrastive learning to extract useful representations of transaction data, followed by fine-tuning with labeled samples. The model delivers state-of-the-art results, including 93.7% precision, 92.3% recall, an F1-score of 92.99%, and an AUPRC of 92.45%. These out-comes underscore the effectiveness of SSL in enhancing detection rates while reducing false positives in real-time environments. Additionally, we explore the scalability and adaptability of SSL-based frameworks in responding to evolving fraud patterns, highlighting their potential to bolster financial security.

关键词： Adaptation models Sensitivity Scalability computational modeling Contrastive learning Credit cards Real-time systems Data models Fraud Tuning

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Optimizing Mobile Charger Scheduling in Wireless Rechargeable Sensor Networks Using Min-Heap Algorithm

Optimizing Mobile Charger Scheduling in Wireless Rechargeabl...

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international Conference on computational intelligence and Communication Networks (CICN)

作者： Md. Kamaruzzaman Abhijit Chandra Department of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

In Wireless Rechargeable Sensor Networks (WRSNs), efficient energy management is crucial for prolonging network lifetime. Mobile Charging Vehicles (MCVs) play a vital role in replenishing the energy of sensor nodes (SNs); but optimizing their scheduling to minimize energy depletion and maximize network coverage remains a significant challenge till date. This paper proposes a novel scheduling approach based on the Min Heap algorithm to optimize the deployment of MCVs in WRSNs. By prioritizing SNs with the lowest remaining energy, the Min-heap algorithm dynamically adjusts the charging sequence, reducing the risk of node failures and improving overall network performance. To show the effectiveness of the proposed scheme, extensive simulations have been done by using MATLAB. Simulation results demonstrate that the proposed approach significantly improves the performance in terms of working time, total energy received, ratio of successful energy replacement and moving distance as compared to traditional scheduling approaches such as EDF and ETLBO.

关键词： Wireless communication Wireless sensor networks Processor scheduling Heuristic algorithms Simulation Scheduling Communication networks MATLAB Energy management computational intelligence

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