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Conceptual Design of Virtual Avatars Based on intelligent Human-Machine Interface in Autonomous Driving 5

Conceptual Design of Virtual Avatars Based on Intelligent Hu...

引用

5th international conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Tang, Runting Wang, Jianmin College of Arts and Media Tongji University Shanghai China

ISBN: (纸本)9798350368284

This study explores the conceptual design of virtual avatars in autonomous driving and their trigger mechanisms in the context of the intelligent Human-Machine Interface (HMI). Through the intelligent system's perception of the situation, virtual avatars can be dynamically triggered based on vehicle status, environmental information, and driver behavior, enabling precise information delivery and real-time feedback. The research indicates that users have high expectations for virtual avatars in terms of safety notifications, emotional expression, and personalized design. This study provides a reference for the design of virtual avatars in future autonomous driving systems and proposes prospects for further adaptive optimization. © 2024 IEEE.

关键词： Virtual environments

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Motion Intent Recognition Algorithm for Lower Limb Rehabilitation Robots Based on Dual-Physics Sensor Feature Fusion

Motion Intent Recognition Algorithm for Lower Limb Rehabilit...

引用

2024 IEEE international conference on Cognitive computing and Complex Data

作者： Lu, Haiyu Zhao, Peng Ma, Fengling Ma, Junhao Wu, Xiangning Gao, Xueshan Beibu Gulf Univ Sch Mech & Shipbldg Ocean Engn Qinzhou Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China Minist Civil Affairs Key Lab Intelligent Control & Rehabil Technol Beijing Peoples R China Guangxi Res Inst Mech Ind CO LTD Nanning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Existing motion intent recognition systems in lower limb rehabilitation robots primarily rely on the fusion of multiple sensor features. Such systems capture the motion characteristics of healthy volunteers during specific m ovements a nd t hen process the data using machine learning algorithms to accurately recognize human motion events, such as forward and backward movements. We address the complexity and inaccuracies of current intent recognition systems by synthesizing feedback from rehabilitation physicians and patients and adopting modular design principles to develop an integrated human motion intent recognition system for lower limb rehabilitation robots. The system utilizes dual physical sensors to collect data on the movement characteristics of the patient's waist, abdomen, and shoulders, which are then classified using the Transformer-LSTM algorithm. The dataset employed for training and testing the algorithm was gathered from a tertiary care hospital, focusing on the movement characteristics of patients with functional hemiplegia of the lower extremities. Clinical trial results demonstrated that the Transformer-LSTM algorithm achieved an average classification accuracy of 97.54% in recognizing human lower limb movement events, compared to 87.48% with the LSTM algorithm. This lower limb rehabilitation robot also significantly enhances patient motivation and comfort during training.

关键词： Human lower limb motion intention recognition Dual Physical Quantity Sensor Transformer-LSTM Algorithm

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Contact-Implicit Model Predictive control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach

Contact-Implicit Model Predictive Control for Dexterous In-h...

引用

2024 international conference on intelligent Robots and systems

作者： Jiang, Yongpeng Yu, Mingrui Zhu, Xinghao Tomizuka, Masayoshi Li, Xiang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing Peoples R China Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

ISBN: (纸本)9798350377712;9798350377705

Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inference, degrading the performance of model-based methods. Inspired by recent advances in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to control dexterous in-hand manipulation and overcome the current limitations. The proposed approach has an attractive feature, which allows the robot to robustly perform long-horizon in-hand manipulation without predefined contact sequences or separate planning procedures. Specifically, we design a high-level contact-implicit model predictive controller to generate real-time contact plans executed by the low-level tracking controller. Compared to other model-based methods, such a long-horizon feature enables replanning and robust execution of contact-rich motions to achieve large displacements in-hand manipulation more efficiently;Compared to existing learning-based methods, the proposed approach achieves dexterity and also generalizes to different objects without any pre-training. Detailed simulations and ablation studies demonstrate the efficiency and effectiveness of our method. It runs at 20Hz on the 23-degree-of-freedom, long-horizon, in-hand object rotation task.

关键词： Predictive control systems

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Future Internet Architecture for IoT Frameworks Implementing the Synapse Algorithm for intelligent Energy Management in Smart Home Automation

Future Internet Architecture for IoT Frameworks Implementing...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Shalini, M. Nagarajan, K.K. Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9798331543617

This paper proposes a novel Internet architecture for IoT frameworks that integrates the Synapse Algorithm for intelligent energy management in smart homes. Current IoT energy management systems often face challenges such as inefficient energy consumption, poor adaptability to user preferences, and limited scalability in managing multiple interconnected devices. These issues are exacerbated by the inability to process large datasets effectively and in real-time. To address these challenges, the proposed framework, intelligent Energy Management using the Synapse Algorithm (IEM-SA), offers a sophisticated solution. The framework leverages the Synapse Algorithm to optimize energy consumption by analysing data from interconnected devices in real- time, learning user behaviour patterns, and dynamically adjusting energy usage based on predicted demand. This not only enhances the system's energy efficiency but also enables seamless control and adaptability across different environments and devices. The proposed method improves energy efficiency, scalability, and adaptability within smart home environments. By integrating real-time data processing and machine learning, it supports the seamless operation of multiple IoT devices while significantly reducing energy waste. Findings indicate that the IEM-SA framework outperforms existing methods in terms of energy savings, system responsiveness, and scalability. Through simulation results, the proposed framework demonstrates its potential to optimize energy usage while improving the overall smart home automation experience. © 2024 IEEE.

关键词： Smart homes

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Data-Driven Predictive control for Robust Exoskeleton Locomotion

Data-Driven Predictive Control for Robust Exoskeleton Locomo...

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2024 international conference on intelligent Robots and systems

作者： Li, Kejun Kim, Jeeseop Xiong, Xiaobin Hamed, Kaveh Akbari Yue, Yisong Ames, Aaron D. CALTECH Dept Computat & Neural Syst Pasadena CA 91125 USA CALTECH Dept Mech & Civil Engn Pasadena CA 91125 USA Univ Wisconsin Madison Dept Mech Engn Madison WI 53706 USA Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA CALTECH Dept Comp & Math Sci Pasadena CA 91125 USA CALTECH Dept Control & Dynam Syst Pasadena CA 91125 USA

ISBN: (纸本)9798350377712;9798350377705

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

关键词： Inverse kinematics

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Enhancing Security Through the Implementation of Deep Learning Techniques GRU and LSTM for intelligent Intrusion Detection

Enhancing Security Through the Implementation of Deep Learni...

引用

2024 international conference on computing and intelligent Reality Technologies, ICCIRT 2024

作者： Malathy, M. Kamalanaban, E. Nayagan, S. Keerthika, R. Jenani, G. Deepika, A. Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Avadi India

ISBN: (纸本)9798331510299

Anomaly-based intelligent intrusion detection systems (IDS) employ advanced algorithms and deep learning (DL) techniques to monitor network traffic, system behavior, and user activities in real-time. By creating a baseline of typical activity, these systems make it possible for them to identify any variations or anomalies that might indicate security risks. This proactive approach enhances the capability to identify and respond to emerging attack vectors that traditional signature-based systems might overlook. This paper proposes an intelligent IDS for predicting security attacks using DL techniques, specifically Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) classifiers. Initially, an input dataset is collected for data preprocessing, where raw data is transformed into a clean dataset. Data preprocessing involves checking for missing values, noisy data, and other inconsistencies before proceeding to data exploration. This step entails utilizing data visualization and statistical techniques to characterize the dataset. Following data preprocessing, the dataset undergoes feature selection to improve predictive model performance and reduce computational costs. The selected features are then classified using GRU and LSTM classifiers. Both LSTM and GRU architectures incorporate gates as an internal mechanism to control the retention or discarding of information, effectively addressing the issues of exploding and vanishing gradients. Finally, the model's performance is evaluated based on metrics such as accuracy, recall, F1 score, and precision. The implementation is carried out using the Python programming language. © 2024 IEEE.

关键词： Long short-term memory

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Learning the Inverse Kinematics of Magnetic Continuum Robot for Teleoperated Navigation

Learning the Inverse Kinematics of Magnetic Continuum Robot ...

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2024 international conference on intelligent Robots and systems

作者： Xiang, Pingyu Qiu, Ke Sun, Danying Zhang, Jingyu Fang, Qin Mi, Xiangyu Wang, Shudong Chen, Mengxiao Wang, Yue Xiong, Rong Lu, Haojian Zhejiang Univ State Key Lab Ind Control & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian Peoples R China Zhejiang Lab Res Ctr Humanoid Sensing Nanhu Headquarters Kechuang Ave Hangzhou 311100 Zhejiang Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Magnetic continuum robots are subject to external magnetic fields and deformed remotely, simplifying the robot's transmission mechanism and providing it with significant potential for miniaturization and operational flexibility. However, modeling magnetic field distribution generated by permanent magnets is complex and requires time-consuming pre-calibrations. Moreover, it is highly susceptible to environments with ferromagnetic materials, posing significant challenges for the control of magnetic continuum robots. In response, we propose an approach that does not overly focus on the magnetic field distribution but instead directly learns the inverse kinematics of magnetic continuum robots end-to-end. Binding the robot's configuration to the pose of external magnets, precise control of continuum robots is facilitated. Additionally, we leverage teleoperation techniques to broaden the applicability of this method. By mounting magnets on a robotic arm and directly utilizing the target pose of the external magnet predicted by a multi-layer perceptron (MLP), we achieve the operation and navigation of magnetic continuum robots in complex environments. Experiments demonstrate that the mean control accuracy along the robot using our learning-based inverse kinematics is about half of the robot's diameter.

关键词： Inverse kinematics

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Real2Sim2Real Transfer for control of Cable-driven Robots via a Differentiable Physics Engine

Real2Sim2Real Transfer for Control of Cable-driven Robots vi...

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IEEE/RSJ international conference on intelligent Robots and systems (IROS)

作者： Wang, Kun Johnson, William R., III Lu, Shiyang Huang, Xiaonan Booth, Joran Kramer-Bottiglio, Rebecca Aanjaneya, Mridul Bekris, Kostas Rutgers State Univ Comp Sci New Brunswick NJ 08901 USA Yale Univ Mech Engn & Mat Sci New Haven CT USA

ISBN: (纸本)9781665491907

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.

关键词： Robots

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Design and Development of An Onboard intelligent Device based on Jetson NX 4

Design and Development of An Onboard Intelligent Device base...

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2023 4th international conference on Computer Engineering and intelligent control, ICCEIC 2023

作者： Yu, Xiaoyong Qin, Liwen Chen, Xin Wu, Lifang Zhang, Biyun Electric Power Research Institute of Guangxi Power Grid Co. Ltd Nanning China Shantou Power Supply Bureau of Guangdong Power Grid Co. Ltd Shantou China

ISBN: (纸本)9798350308877

This paper presents the design and development of an onboard intelligent device based on Jetson Xavier NX. The device utilizes the computational and recognition capabilities of AI chips to analyze safety signs and provide feedback, assisting in inspections. The technological components and design of the onboard intelligent device are discussed, including the structural framework of embedded systems built with AI chips, as well as the components and applications of embedded systems. The DJI Maverick series models, M30 and M300, are selected and their applications and performance in the power grid industry are introduced. The unmanned aircraft and onboard intelligent device are connected through the DJI Maverick series interface for expansion and control. Edge computing is implemented using a training and computing separation model, and a development roadmap is provided. The designed onboard intelligent device is installed on DJI drones to real-time recognize video streams captured by the drones, which includes modules for data collection, computational analysis, wireless communication, storage, input/output communication, and power interfaces. Through this design, intelligent identification and real-time monitoring of public safety in the power sector can be achieved. © 2023 IEEE.

关键词： Edge computing

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FMUGym: An Interface for Reinforcement Learning-based control of Functional Mock-up Units under Uncertainties 31

FMUGym: An Interface for Reinforcement Learning-based Contro...

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31st international Workshop on intelligent computing in Engineering, EG-ICE 2024

作者： Wrede, Konstantin Huang, Chenzi Wohlfahrt, Tommy Hartmann, Nick Fraunhofer Institute for Integrated Circuits IIS Division Engineering of Adaptive Systems EAS Germany

Uncertainties complicate the task of designing optimal controllers for complex systems. This work introduces FMUGym, a novel open source interface that connects reinforcement learning libraries following the Gymnasium standard with co-simulation Functional Mock-up Units. As the latter encapsulate the model of the control plant, FMUGym can transform them into an environment of a reinforcement learning setup. FMUGym allows to inject uncertainties into system dynamics, inputs and outputs during training. This fosters robust control policies that adapt to possible variations and aims to bridge the simulation-to-reality gap. We demonstrate FMUGym's effectiveness by training an agent to control a nonlinear system with and without uncertainties, highlighting the benefit of noise injection. A second example showcases applicability in heating, ventilation and air conditioning systems. The source code and additional resources for this project are available on GitHub (https://***/Fraunhofer-IIS/fmugym), with further development planned based on community feedback. © 2024 Proceedings of the 31st international Workshop on intelligent computing in Engineering, EG-ICE 2024. All rights reserved.

关键词： Air conditioning

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