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检索条件"任意字段=Proceedings of the 2020 2nd International Conference on Robotics Systems and Vehicle Technology"

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Electric vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation systems 2

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Siva Ramkumar, M. Sivaramkrishnan, M. Uday, Kaparthi Kanan, Mohammad Mohan Raj, C. Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India CVR College of Engineering EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS. © 2025 IEEE.

关键词： Reinforcement learning

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APPLICATIONS And PROSPECTS OF AI systems IN AUTOMATIC NAVIGATION 2

APPLICATIONS AND PROSPECTS OF AI SYSTEMS IN AUTOMATIC NAVIGA...

引用

2nd international conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Wu, Chengtian Big Bridge Academy Wuxi214000 China

ISBN: (纸本)9781837242672

With the rapid development of society, the application of artificial intelligence (AI) is becoming more and more prevalent in daily lives, particularly in the aspects of automatic navigation systems of vehicles. Nevertheless, the traditional automatic navigation system often led to inconsistent performance in intricate circumstances. They cannot deal with dynamic situations in emergent conditions, which impedes the spread in real-life applications. This paper focuses on the prospect of AI systems to improve the accuracy, precision, and reliability of navigation systems. Hence, the system's decision-making precise and operational constancy is largely enhanced by combining sensor data with AI. The research results demonstrate that AI systems using deep learning algorithms showed appropriate performance in simulation tests, which is significant because it not only indicates the practical application capabilities of automatic navigation systems but also lays the foundation for future advancements of AI technology in intelligent transportation and robotics. © The Institution of Engineering & technology 2024.

关键词： Deep learning

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Multi-Robot Cooperative Navigation in Dynamic Environments using Deep Reinforcement Learning in ROS 24

Multi-Robot Cooperative Navigation in Dynamic Environments u...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wu, Shuangshuang Wu, Jianchuang Chen, Wenbai Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798400710681

In the rapidly advancing field of robotics, multi-robot cooperative navigation stands out as a critical area with applications ranging from indoor environmental monitoring to logistics. However, achieving autonomous navigation in dynamic settings without preexisting maps poses considerable challenges, notably in terms of efficiency. This study applies Deep Reinforcement Learning (DRL) within the Robot Operating System (ROS) framework to achieve the autonomous navigation and overcome resource planning in such environments. By incorporating DRL into the global path planner of ROS, robots are able to learn and execute cooperative navigation strategies in real-time, without relying on prior environmental knowledge. Our simulation experiments revealed that this integration not only improves navigation efficiency but also minimizes resource consumption. The results underscore the trans-formative potential of DRL in optimizing individual and collective robot navigation strategies. It offers new insights and approaches for the application of multi-robot systems in complex dynamic environments, with significant theoretical and practical implications. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Disturbance observer-based consensus of leader-following multi-agent systems via aperiodic intermittent distributed event-triggered control 24

Disturbance observer-based consensus of leader-following mul...

引用

2nd international conference on Artificial Intelligence, systems and Network Security, AISNS 2024

作者： Zhou, Yuting Luo, Yiping Liu, Yutao Hunan Institute of Engineering Hunan Xiangtan China

ISBN: (纸本)9798400711237

This article investigates a class of consensus problems of leader-following multi-agent systems with unknown external disturbance. A disturbance observer for a type of a leader-following multi-agent system with unknown external disturbance is designed to estimate external disturbance. An aperiodic intermittent distributed event-triggered control protocol is proposed on the basis of the estimated external disturbance information and local relative information. By constructing an appropriate Lyapunov function on the basis of Lyapunov stability theory, sufficient conditions for achieving the consensus control of the leader-following multi-agent system with unknown external disturbance are obtained. Furthermore, the designed aperiodic intermittent event-triggered control protocol is demonstrated to be able to avoid Zeno behavior. Finally, a numerical example is given to verify the feasibility of the theoretical results. © 2024 Copyright held by the owner/author(s).

关键词： Lyapunov functions

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Reconfigurable Smart Grid Management for Electric vehicle Charging and the Integration of Renewable Energy Sources using Pied Kingfisher Optimizer 2

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sivaramkrishnan, M. Ramkumar, M. Siva Uday, Kaparthi Kanan, Mohammad Raj, C. Mohan Giri, Jayant Karpagam College of Engnieering Dept of EEE India SNS College of Technology Dept of ECE India CVR College of Engineering Ibrahimpatanam EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

Electric vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. Integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Wireless Ranging and Smelting Driving Location based on Moore Voting Method 2

Wireless Ranging and Smelting Driving Location based on Moor...

引用

2nd international conference on Innovations and Development of Information Technologies and robotics, IDITR 2023

作者： Liu, Jinhao Lv, Zhiyong Xu, Jiahui Hu, Yiting Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan China Wuhan University of Technology Guangdong Inland Port and Shipping Research Co. Ltd. Wuhan China Wuhan University of Technology School of Automation Wuhan China

ISBN: (纸本)9798350345575

In order to explore the reasons for the insufficient positioning accuracy of the crane, to ensure the personal safety of the staff, and to improve the reliability of production operations and work efficiency. Focus on building a set of practical, easy-To-operate, concise and efficient real-Time positioning system based on TOF wireless ranging technology and laser radar technology measurement principle. Analyze the principle of the TOF wireless ranging data error, and use the extended Kalman filter method to perform error analysis and noise reduction processing on the distance data collected by the TOF wireless ranging sensor on site. After the extended Kalman filtering data, the average error is less than 0.1 cm, to improve the reliability and robustness of real-Time positioning systems for on-site operations in steel mills. The Moore voting method is used to locate the smelting crane laterally, combined with the distance data of the laser radar sensor to determine the longitudinal positioning of the wireless smelting crane, and realize the all-round real-Time positioning function of the smelting crane during operation. © 2023 IEEE.

关键词： Optical radar

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A Survey on Cooperative Control of Multi-Agent systems 2

A Survey on Cooperative Control of Multi-Agent Systems

引用

2nd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Yang, Zecheng Ma, Nan Yao, Yongqiang Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Beijing University of Technology Faculty of Information and Technology Beijing100124 China Beijing Shuncheng High-tech Co. Ltd Beijing China

ISBN: (纸本)9798350360363

Cooperative control of multi-agent systems involves the coordination and collaboration of multiple intelligent agents to achieve common goals or tasks. This field finds extensive applications in robotics, intelligent transportation systems, the Internet of Things, and unmanned systems, garnering substantial attention from both academia and industry. Moreover, it serves as a fundamental requirement for intelligent interaction and human-robot collaboration, offering crucial support for machines in perceiving their external environments. Over the last decade, notable strides have been made in this domain. Consequently, this paper conducts a comprehensive review of recent advancements. Initially, this paper delineate the backdrop of cooperative control in multi-agent systems, followed by an exploration of the research's progression. Subsequently, various methodologies for formation and collaboration are introduced, with an in-depth discussion of advanced cooperative control algorithms for multi-agent systems. Lastly, this paper provides a prospective outlook on the future direction of multi-agent cooperative control. © 2023 IEEE.

关键词： Multi agent systems

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INTELLIGENT ENHANCEMENT SOLUTIONS FOR IndUSTRIAL ROBOTS USING AI technology 2

INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USIN...

引用

2nd international conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Wu, Di School of International Education Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9781837242672

The integration of artificial intelligence (AI) into industrial robotics has revolutionized manufacturing by improving various aspects such as precision, speed, and energy efficiency. This paper delves into AI-driven solutions aimed at enhancing the performance of industrial robots, focusing on the application of advanced algorithms, machine learning models, and AI frameworks. Through a comprehensive performance comparison between AI-enhanced robotic systems and traditional methods, this study evaluates several key performance indicators (KPIs), including accuracy, flexibility, operational efficiency, and sustainability. The results underscore AI's transformative role in increasing overall system performance, achieving faster processing times, reducing energy consumption, and enhancing adaptability in complex manufacturing environments. Additionally, this paper explores the implications of these AI advancements for industrial automation, highlighting areas for future research, such as the optimization of AI algorithms, scalability across diverse industries, and improved integration with existing robotic systems. These findings emphasize the long-term potential of AI to reshape industrial automation, driving forward innovation in smart manufacturing and fostering sustainable development across the sector. © The Institution of Engineering & technology 2024.

关键词： Smart manufacturing

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Deep Reinforcement Learning: Key Algorithms, Applications, and Future Challenges 2

Deep Reinforcement Learning: Key Algorithms, Applications, a...

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2nd international conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Anusha, S. Nithyanandhan, R. Anish, T.P. Girija, P. Nalini, M. Siva Subramanian, R. Easwari Engineering College Department of Information Technology India S A Engineering College Dept of Cse Chennai India R.M.K. College of Engineering and Technology Dept of Cse Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept of Cse India S A Engineering College Dept of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798350368413

Deep Reinforcement Learning (DRL) is a field of study that is growing very fast and brings together reinforcement learning and deep learning for agents who learn complex behaviors from observations from the environment. This integration enables DRL to solve tasks that before could not be solved with ordinary RL techniques. DRL has proven itself across multiple categories including robotics, gaming including AlphaGo and OpenAI Five, finance including sophisticated trading strategies and portfolio management, healthcare including treatment planning and interpretation of medical images and autonomous vehicles including decision making and path planning. The primary goals of this survey are introducing DRL and its fundamental ideas, reviewing major algorithms and methods in DRL, describing advanced research topics and methods, and exhibiting versatile applications. Furthermore, the survey intends to establish current contradictions and recommend further work and research agendas to enhance the study of DRL. In this way, this survey would be useful for researchers and practitioners who are interested in studying and advancing the field of DRL. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on the Recent Trends and Challenges on the Adoption of robotics in the Healthcare Sector 2

Research on the Recent Trends and Challenges on the Adoption...

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2nd international conference on Edge Computing and Applications, ICECAA 2023

作者： Saini, Ramesh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350347579

Modern technology is expanding quickly throughout many industries, especially healthcare. Hospitals were able to obtain a significant advantage for several technological developments in the medical sector, including the use of artificial intelligence (AI), IoT, and robotics. One such technology that has enabled clinics to build out a special niche is robotics because of the increased accuracy, mechanization, efficiency, and decreased length that these innovations provide. All these things aid medical professionals in becoming more proficient and flexible. In addition to medical practitioners embracing robots, they must have a strong technological understanding, a perceptive capacity for learning, and a willingness to tackle novel opportunities. Less intrusive treatments, personalized and regular surveillance of people who have long-term illnesses, sophisticated medicines, and community interactions for aged people are all supported by the use of robotic systems. When patients are in such dire straits, it may be difficult for health practitioners to do their jobs. People have responsibilities and must take care of themselves at the identical period. Robotic technologies are rapidly becoming the ideal resource for medical staff in this catastrophic era. This study examines the most up-to-date developments and obstacles surrounding healthcare robot adoption. The healthcare system is only beginning to utilize automation, thus there is plenty of room for more basic studies in this field. © 2023 IEEE.

关键词： robotics

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