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检索条件"任意字段=17th International Conference on Distributed Computing and Intelligent Technology"

共 4256 条记录，以下是11-20 订阅

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Optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge 21st

Optimal Dispersion in Triangular Grids: Achieving Efficiency...

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21st international conference on distributed computing and intelligent technology

作者： Pandit, Himani Pandit, Supantha Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India

ISBN: (纸本)9783031814037;9783031814044

In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. We address this challenge on an anonymous triangular grid graph, where each vertex can connect to up to six adjacent vertices. We propose a distributed deterministic algorithm that achieves dispersion on an unoriented triangular grid graph in O(root n) time, where n is the number of vertices. Each robot requires O(log n) bits of memory. the time complexity of our algorithm and the memory usage per robot are optimal. this work builds on previous studies by Kshemkalyani et al. [WALCOM 2020 [17]] and Banerjee et al. [ALGOWIN 2024 [3]]. Importantly, our algorithm terminates without requiring prior knowledge of n and resolves a question posed by Banerjee et al. [ALGOWIN 2024 [3]].

关键词： distributed algorithms Mobile robots Dispersion Triangular grid Deterministic algorithms

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19th international conference on distributed computing and intelligent technology, ICDCIT 2023

19th International Conference on Distributed Computing and I...

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19th international conference on distributed computing and intelligent technology, ICDCIT 2023

ISBN: (纸本)9783031248474

the proceedings contain 29 papers. the special focus in this conference is on distributed computing and intelligent technology. the topics include: Mapped-RRT* a Sampling Based Mobile Path Planner Algorithm;intelligent Optimization Algorithms for Disruptive Anti-covering Location Problem;enhancing Robustness of Malware Detection Model Against White Box Adversarial Attacks;early Detection of Covid Using Spectral Analysis of Cough and Deep Convolutional Neural Network;analysis of Tweets with Emoticons for Sentiment Detection Using Classification Techniques;fine-Tuning of Multilingual Models for Sentiment Classification in Code-Mixed Indian Language Texts;landslide Classification Using Deep Convolutional Neural Network with Synthetic Minority Oversampling Technique;ALPR - An intelligent Approach Towards Detection and Recognition of License Plates in Uncontrolled Environments;towards Railway Cable Infrastructure Protection: Turning Cross-Sectional Explorative Analytics to Answers;WANMS: A Makespan, Energy, and Reliability Aware Scheduling Algorithm for Workflow Scheduling in Multi-processor Systems;designing an Intangible Tele-Interaction for Point-to-Point Robot Control Using Coercive Gesture Filtering;sentiment Analytics for Crypto Pre and Post Covid: Topic Modeling;a Rough Set Based Approach to Compute Impact of Non Academic Parameters on Academic Performance;varta Rasa - A Simple and Accurate System for Emotion Recognition in Conversations;an Optimal Approach for Multi-class Object Detection;Performance Analysis of Routing Protocol for Low Power and Lossy Networks (RPL) for IoT Environment;A Novel Image Steganography Technique Using AES Encryption in DCT Domain;Text Classification Using Correlation Based Feature Selection on Multi-layer ELM Feature Space;multi-objective Pelican Optimization Algorithm for Engineering Design Problems;prediction of Accident and Accident Severity Based on Heterogeneous Data;multiple Criteria Decision Making-Based Task Offloading and Sch

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distributed Formation Coordinated Control of Multiple Unmanned Surface Vehicles Based on Deep Reinforcement Learning 17th

Distributed Formation Coordinated Control of Multiple Unmann...

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17th international conference on intelligent Robotics and Applications

作者： Luo, Fangyou Wang, Tao Gao, Chen Rao, Zihao Liu, Bo Sun Yat Sen Univ Sch Intelligent Engn Shenzhen 518107 Peoples R China

ISBN: (纸本)9789819607730;9789819607747

Over the last decade, Unmanned Surface Vehicles (USVs) have gained significant traction in marine scientific research and military domains. Given the necessity for extensive coverage in many missions, the deployment of multiple USVs for coordinated operations has emerged as a viable strategy. this study presents a novel model-free deep reinforcement learning (DRL) approach for coordinating multiple USVs, with a particular emphasis on ensuring formation stability. First, a dynamic model for formation navigation in a non-stationary stochastic ocean environment is derived, accounting for both acceleration and angular velocity control. Next, a hierarchical leader-follower architecture is designed, facilitating the formation of stable chain formations and simplifying the control challenge. To address the complex, nonlinear coupling issues, deep reinforcement learning methods are employed, specifically the deep deterministic policy gradient (DDPG) and double deep Q-network (DDQN), both utilizing deep neural network (DNN) approximations. the comparative analysis of these methods in simulation showcases the effectiveness of the proposed control strategy and the adaptability of USVs in maintaining formation navigation even in non-stationary random environments.

关键词： Unmanned surface vehicles Formation control Hierarchical leader-follower architecture Deep reinforcement learning Double deep Q-network

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

the advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. the proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. the predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. this research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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Partial distributed Deep Learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th international conference on Electronic Information technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. the latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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Research on intelligent Dynamic Obstacle Avoidance Strategy for Space Collaborative Robots 17th

Research on Intelligent Dynamic Obstacle Avoidance Strategy ...

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17th international conference on intelligent Robotics and Applications

作者： Wang, Yaobing Xie, Fanglin Zhang, Yahang Wang, Lingxin China Acad Space Technol Beijing 100094 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

To achieve the great safety requirements for space collaborative robots and astronauts working in remote, unknown and complex space environment, the robots should be clever enough to avoid moving human or obstacles in a quite short time to keep both safe. In this paper, Deep Reinforcement Learning (DRL) techniques were applied to robot manipulators with a grasping workspace invaded by unpredictable moving obstacles. By introducing a hierarchical curriculum learning framework, a multidimensional agent was trained in a high-precision virtual environment. the dynamic obstacle avoidance success rate of strategy trained by Soft Actor-Critic (SAC) achieves 94.6%, higher than 68.4% by Deep Deterministic Policy Gradient (DDPG). then the Actor network's weight and bias matrices were copied and successfully implanted in a real RM63-6F anthropomorphic robot manipulator. the robot manipulator's end clamp can reach about 0.02m near the target site, which verifies that using DRL to obtain the motion planning strategy of space robot manipulator with limited computing resources is feasible.

关键词： space collaborative robots deep reinforcement learning motion planning dynamic obstacle avoidance strategy sim-to-real

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intelligent Algorithm for Optimizing Delivery Time in Electric Vehicle Fleet Systems 17

Intelligent Algorithm for Optimizing Delivery Time in Electr...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Vasantha, Chanakya Mayank, Jaishree Agrawal, Aman Mondal, Arijit Indian Institute of Information Technology Design and Manufacturing Kancheepuram India Indian Institute of Technology Patna India

ISBN: (纸本)9798331531195

the proliferation of electric vehicles (EVs) in the transportation sector has led to many immediate problems. this work focuses on efficiently managing a fleet of EVs for logistic supply-chain management. intelligent planning is essential to avoid unfruitful waiting at a charging station, finding a time-efficient path for EVs, and better utilization of resources. this work proposes effective methods for an electric vehicle (EV) delivery system that considers the heterogeneity of the EVs, which vary in battery power and load capacity. the objective is to identify optimal routes for electric vehicles to deliver items throughout a city network and return to the depot, aiming to minimize the longest travel time. We formulate the Constrained Optimization Problem to find optimal solutions. However, due to the NP-hardness of this problem, we find the limitations in scalability for computing exact solutions. To address this, we propose an AI-based Simulated Annealing (SA) algorithm designed to compute near-optimal solutions. Additionally, we introduce an efficient local search heuristic to further reduce computation time. Our experimental findings demonstrate that the proposed heuristic method can determine reasonable routes for vehicles with a performance deviation of less than 25% from the SA solution. © 2025 IEEE.

关键词： Constrained optimization

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Collaborative Pushing and Grasping in Complex Scenarios via a Generator-Evaluator Network with Multiple Action Primitives 17th

Collaborative Pushing and Grasping in Complex Scenarios vi...

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17th international conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. this paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. this strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Achieving time-critical control in Edge Robotics Systems over 5G 17

Achieving time-critical control in Edge Robotics Systems ove...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Baxi, Amit Mageshkumar, Vincent Murthy, Girish S. Merwaday, Arvind Raju, Arvind Hosamane, Gowtham Prabhu, Sugnan Natarajan, Venkat Sudhakaran, Susruth Cavalcanti, Dave Hatalkar, Atul N. Maciocco, Christian Iyer, Ravishankar Vannithamby, Rath Intel Technology India Private Limited Bangalore India Intel Corporation HillsboroOR United States

ISBN: (纸本)9798331531195

In this paper, we present the challenges and solutions to achieve time-critical robot control in an Edge computing System, when the sensing-computing-control functions are distributed and connected over a 5G network. We describe these challenges/solutions in context of a real 5G Edge Robotics System testbed, where the task for a robot arm is to pick up moving objects from a conveyer belt but with vision perception and other AI computing workloads running remotely on an Edge computing Server, connected over 5G. We present a learning-based compute-communications-control Co-design approach which enables (a) robust robotic control over an unreliable wireless network and (b) dynamic QoS adaptation to reduce network traffic. In addition, we also highlight simulation-to-real challenges and strategies in deploying reinforcement learning policies trained in simulation to control a real robot arm. Finally, we present overall architecture, experimental results, and experiences in enabling a real-world 5G Edge Robotic system. © 2025 IEEE.

关键词： 5G mobile communication systems

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Design and Development of a Modular Pneumatic-Driven Soft Tripod Robot 17th

Design and Development of a Modular Pneumatic-Driven Soft Tr...

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17th international conference on intelligent Robotics and Applications

作者： Zhang, Yu Luan, Lingkai Zhang, Longchao Li, Xianglong Zheng, Tianjiao Zhu, Yanhe Zhang, Zongwei Wang, Tianshuo Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Complex Intelligent Sys Harbin 150080 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China Jihua Lab Foshan 528200 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

the flexibility and adaptability of soft legged robots offer great promise for their application in complex environments. this paper presents the design of a modular pneumatic-driven soft tripod robot (MPSTR), where the soft mechanical legs consist of two bending modules with S-shaped curvature structures. Each module comprises three uniformly distributed air chambers and a tube slot, and by introducing constraints from fiber-reinforced structures, each leg possesses planar mobility. To achieve control of the soft mechanical legs, this study establishes both the forward and inverse kinematics of the soft legs, and tests and fits the relationship between input chamber pressure and chamber extension length. To further enhance the performance and stability of the mechanical legs, we construct an experimental platform for the MPSTR, testing the translational capabilities of a single soft leg along the X and Y axes, and analyzing the feasibility of coordinated tripod locomotion and rotation gait planning.

关键词： Soft Mechanical Legs Mirrored Bending Pneumatic-Driven

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