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Advanced Deep Reinforcement Learning Strategies for Enhanced Autonomous Vehicle Navigation systems 2

Advanced Deep Reinforcement Learning Strategies for Enhanced...

引用

2nd International conference on computer, Communication and control, IC4 2024

作者： Dhinakaran, M. Rajasekaran, R.Thalapathi Balaji, V. Aarthi, V. Ambika, S. Galgotias College of Engineering & Technology Knowledge Park Ii Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9798350387933

The objective of this research is to investigate more sophisticated techniques in Deep Reinforcement Learning (DRL) for the purpose of improving navigation systems for autonomous vehicles. The results of our research indicate that the success rates of DRL algorithms are encouraging: DQN (75.2%), PPO (82.6%), and TRPO (78.1%). The success of navigation is highly influenced by optimisation factors such as parameter tuning (87.4%), exploration-exploitation balance (85.1%), and reward shaping (88.9%). The generalisation score for transfer learning is rather good (92.3%), while the safety modules are designed to make the system safer. These findings highlight the significance of algorithm selection, parameter optimisation, and advanced techniques in the process of optimising navigation for autonomous vehicles, hence establishing essential benchmarks for future developments. © 2024 IEEE.

关键词： Navigation systems

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Research and Implementation of computer Vision and Internet of Things in Smart Homes 7

Research and Implementation of Computer Vision and Internet ...

引用

7th IEEE Information Technology, Networking, Electronic and Automation control conference, ITNEC 2024

作者： Wang, Lingwei Zhu, Fuyu Wang, Hua College of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798350370782

This paper comprehensively discusses the application research and implementation of computer vision and the Internet of Things in the field of smart homes. The paper first reviews the basic theories and technical principles of computer vision and the Internet of Things. It then conducts an in-depth analysis of the specific applications and evaluates the effects of these two technologies in intelligent security systems, environmental control, health monitoring, etc., through multiple experimental cases. The experimental results demonstrate that the combination of computer vision and the Internet of Things can effectively enhance the performance of smart home systems, optimize user experience, and has broad application prospects. future research needs to further explore the integration and application of new technologies and address issues such as user privacy and data security to promote continued innovation and development in the smart home field. © 2024 IEEE.

关键词： Smart manufacturing

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A STUDY ON MOTION control SYSTEM FOR REHABILITATION ROBOT BASED ON MULTI-SENSOR FUSION

A STUDY ON MOTION CONTROL SYSTEM FOR REHABILITATION ROBOT BA...

引用

2024 International conference on Electrical Engineering and Intelligent control, EEIC 2024

作者： Ding, Yunfei School of Information Science and Technology Fudan University Shanghai200000 China

ISBN: (纸本)9781837242672

Rehabilitation robots play a significant role in helping rehabilitation therapy. An effective motion control system is crucial for making the movement of rehabilitation robots precise, flexible, and intelligent. Introducing sensors into motion control systems can bring a feedback control system based on motion intent recognition, which optimizes the adaptive control for rehabilitation robots and provides a more comfortable experience for patients. The study aims to improve patient experience by examining the architecture, algorithms, and techniques of rehabilitation robot control systems. Actual experience indicates that feature layer and decision layer fusion are more suitable for the control system of rehabilitation robots. Due to the accuracy and high computational speed required for rehabilitation robot control systems, many novel algorithms and techniques have been applied in the motion control system to extract features, recognize motion intents and complete human-computer interaction. In the future, the challenge for rehabilitation robots is to develop more accurate and faster algorithms to minimize the usage disadvantage caused by latency;and to explore appropriate algorithms for rehabilitation robots applied in different settings. © The Institution of Engineering & Technology 2024.

关键词： Feedback control

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Playing Cards Classification and Detection Using Sequential CNN Model Through Machine Learning Techniques Using Artificial Intelligence

Playing Cards Classification and Detection Using Sequential ...

引用

2024 International conference on E-Mobility, Power control and Smart systems, ICEMPS 2024

作者： Mittal, Khushi Gill, Kanwarpartap Singh Chauhan, Rahul Sharma, Manish Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun248002 India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350394399

The task of identifying and classifying playing cards within images or video streams in computer vision involves employing a Sequential Convolutional Neural Network (CNN) model. This process combines image categorization and object recognition, presenting a sophisticated and engaging implementation of machine learning techniques. The significance of classifying and detecting playing cards extends to artificial intelligence and computer vision, with numerous practical applications. systems for classifying and detecting playing cards utilizing artificial intelligence (AI) rely on computer vision methods and deep learning algorithms to achieve precise identification and categorization. These technological solutions not only enhance automation and efficiency across various tasks but also open up new possibilities for interaction and enjoyment in the domain of playing card games, making them accessible and engaging in diverse industries and user experiences. The primary objective of this research is to categorize playing cards and predict their expected accuracy, targeting a result of 75 percent. The high level of accuracy demonstrated in this study holds potential benefits for future researchers exploring this technology and subject matter. © 2024 IEEE.

关键词： computer vision

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Design and control of a Novel Soft-Rigid Lower Limb Exoskeleton Robot

Design and Control of a Novel Soft-Rigid Lower Limb Exoskele...

引用

2024 International conference on Intelligent Robots and systems

作者： Wang, Yuxuan Yuan, Shaoke Pu, Zihan Wang, Jiangbei Fei, Yanqiong Shanghai Jiao Tong Univ Res Inst Robot Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Jiao Tong Univ ShenZhen Res Inst Shanghai Peoples R China

ISBN: (纸本)9798350377712;9798350377705

This paper presents a study on the design and control of a novel soft-rigid lower limb exoskeleton robot. First, based on anatomy, a novel exoskeleton structure design is proposed that applies Curl Pneumatic Artificial Muscles (CPAMs) to lower limb joints to actuate lower limb movement, and transmit force and motion through rigid parts. A phenomenological characteristic model of the CPAM based on experimental tests and fitting methods is constructed for exoskeleton control. Then, a feedforward-feedback hybrid control method based on four CPAM characteristic models is proposed to improve the control accuracy and stability of the exoskeleton. A human-in-the-loop control method based on human-robot hybrid dynamics modeling and human intentions and states is proposed to improve the human-robot interaction performance of exoskeletons. Experimental results show that feedforward-feedback hybrid control can reduce the maximum tracking error of the exoskeleton to 3.4% for hip, 2.9% for knee, and 4.7% for ankle joint. The exoskeleton can achieve intentional control based on EMG signals. With the assistance of the exoskeleton, the muscle activity of the human lower limbs is reduced by an average of 32.2%. The proposed soft-rigid lower limb exoskeleton robot has the advantages of being lightweight, having good flexibility, being comfortable wearing and having good human-computer interaction, which can improve efficiency. In the future, it will provide more effective intelligent rehabilitation equipment for patients with lower limb movement disorders.

关键词： Exoskeleton (Robotics)

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Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo

Real-Time Drone System for Detecting, Tracking, and Followin...

引用

2025 International conference on control, Automation, and Instrumentation, IC2AI 2025

作者： Salman, Ali Dika, Mohammad Diab, Hassan Owayjan, Michel Achkar, Roger American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evaluates the detecting accuracy by varying the distance between the drone and the target object, addressing gaps in current research through a detailed practical insight. This research explores key aspects of drone-based video surveillance, including object detecting, tracking and real-time following, aiming to enhance understanding and methodology in computer Vision (CV) applications with Unmanned Aerial Vehicles (UAVs). The Simulation of a surveillance drone system in Gazebo simulator within the ROS2 framework. The drone is programmed to dynamically follow and track a mobile robot, enabling systematic analysis of the object detection algorithm's performance. The findings contribute to advancing the reliability and effectiveness of drone-based video surveillance systems for future innovations in security and monitoring applications. © 2025 IEEE.

关键词： Drones

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Koopman-based Deep Learning for Nonlinear System Estimation 63

Koopman-based Deep Learning for Nonlinear System Estimation

引用

63rd IEEE conference on Decision and control, CDC 2024

作者： Sun, Zexin Chen, Mingyu Baillieul, John Boston University Division of Systems Engineering United States Boston University Department of Electrical and Computer Engineering United States Boston University Division of Systems Engineering Departments of Mechanical Engineering Electrical and Computer Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning. © 2024 IEEE.

关键词： Deep reinforcement learning

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Improved Neural Network Model Predictive control based on DTBO algorithm: Applied to Manipulator Robot 1

Improved Neural Network Model Predictive Control based on DT...

引用

1st International conference on Electrical, computer, Telecommunication and Energy Technologies

作者： Stiti, Chafea Benrabah, Mohamed Aouaichia, Abdelhadi Kara, Kamel Blida1 Univ Lab Syst Elect & Telecommande Blida Algeria USTHB Univ Lab Robot Parallelisme & Syst Embarques Algiers Algeria

ISBN: (纸本)9798350388497;9798350388480

This study suggests an Improved Neural Model Predictive control approach based on the metaheuristic Driving-Training-Based-Optimization (INNMPC-DTBO) applied to control a two degree-of-freedom manipulator robot. This control strategy uses two neural network models of the Multi-Layer-Perceptron (MLP) type as a models for predicting the future angular positions of the manipulator robot, and the DTBO algorithm as an on-line solver of the improved NNMPC optimization problem which contain our contribution, which is the adding of a sequence of reduction weighting coefficients over the prediction horizon. To analyze the effectiveness of the suggested approach, a comparative study was carried out by comparing the obtained outcomes and those of distinct techniques, specifically the optimized PID by particle swarm optimization algorithm controller, model predictive control based on a neural network using teaching-learning-based optimization and driving-training-based-optimization. The obtained results demonstrate that the proposed approach offers satisfactory computing time and accuracy, which means that it can control non-linear constrained systems with fast dynamics.

关键词： MPC Neural Networks DTBO MLP Weighting Factor Computing Time Manipulator Robot

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Hardware-in-The-Loop-Based Validation of Distribution System control Applications with Grid Operators, Customer and Market Participants 4th

Hardware-in-The-Loop-Based Validation of Distribution System...

引用

4th Energy Informatics Academy conference

作者： Otte, Marcel Krueger, Carsten Das, Pratyush Rohjans, Sebastian Lehnhoff, Sebastian OFFIS Inst Informat Technol Oldenburg Germany Carl von Ossietzky Univ Oldenburg Oldenburg Germany Jade Univ Appl Sci Oldenburg Germany

ISBN: (纸本)9783031747373;9783031747380

The flexibility potential of electric vehicles, heat pumps, photovoltaics, and storage systems allocated at customer premises raises attention to grid-serving and market-serving control applications. However, control applications vary as the kind of flexibility differs due to the complexity of interactions behind the meter, the actors involved, conflict of interests, regulatory requirements, and the number of systems to integrate. Consequently, the validation of applications across all phases of development reveals the potential bottlenecks of the application before deployment. This work presents the use cases for distribution system control applications and how they can be validated in a hardware-in-the-loop simulation environment. The validation environment covers the systems of a distribution system operator and its upstream grid operator, virtual field devices, a real-time grid simulation, the meter operator including the operation of an advanced metering infrastructure, as well as applications from market participants like aggregators. As a proof of concept, two use cases are selected to demonstrate how grid operator coordination and customer behaviour can be validated. Thereafter, it will be implied how the validation environment is suitable for future use cases, such as the coordination of grid-serving and market-serving control applications.

关键词： active distribution grid flexibility provision validation environment smart meter customer premises

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control Strategies and Battery Management for Hybrid Energy systems and Electric Vehicles

Control Strategies and Battery Management for Hybrid Energy ...

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2024 International conference on Electrical and computer Engineering Researches, ICECER 2024

作者： Dlamini, Sabelo Mendu, Bongumsa Monchussi, Bessie University of South Africa Department of Electrical and Smart Systems Engineering Pretoria South Africa National Transmission Company of South Africa SOC Ltd. Asset Management Department Johannesburg South Africa

ISBN: (纸本)9798331539733

Battery management systems (BMS) play a vital role in enhancing battery performance, ensuring safety, and prolonging lifespan through accurate monitoring, temperature control, and cell balancing. Several energy management strategies have been developed to optimize the distribution of power between energy sources, leading to improved fuel efficiency and better vehicle performance. This paper provides an in-depth review of control strategies and battery management for hybrid energy systems and electric vehicles will be presented. A systematic methodology was employed which involved an extremely thorough and careful approach to identify, select, and evaluate an appropriately connected research documents. The employed methodology included developing of search keywords, search for information from Scopus database, screening and choosing studies based on predefined criteria and synthesizing the findings. This study will examine recent developments in electric vehicle charging, hybrid energy systems, energy harvesting, and solar technologies, focusing on control methods, power conversion, and optimization. It will also explore microcontroller-based systems in energy, robotics, and healthcare. The research will conclude with proposals for future studies, providing direction for ongoing advancements in these areas. © 2024 IEEE.

关键词： Hybrid power

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