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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

共 15865 条记录，以下是111-120 订阅

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Gait phase detection based on inception-self-attention 6

Gait phase detection based on inception-self-attention

引用

6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Yang, Chunbo Zheng, Jianbin Gao, Yifan Yan, Ruoxi Wang, Chaojie Wang, Yu College of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798331541699

Gait analysis is an important research direction in motion monitoring and lower limb exoskeleton. Based on real IMU data and ground reaction force data, this paper proposes a deep learning model that combines Inception and Self-Attention mechanisms for gait phase classification. The experiment uses multi-time step input to perform classification tasks under 4-phase, 5-phase, and 6-phase gait division conditions and comprehensively analyzes the impact of model parameters on performance. The experimental results show that the classification accuracy of the proposed method under different divisions reaches 97.85%, 96.77%, and 96.37% respectively, reflecting the model's adaptability to different gait fine-grained classification tasks. This study provides effective support for the application of gait recognition technology in the development of smart wearable devices and motion monitoring and also lays a theoretical and practical foundation for in-depth research on gait phase classification. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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A Critical Review on the Future of robotics in Manufacturing 3

A Critical Review on the Future of Robotics in Manufacturing

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Agarwal, Neeraj Yadav, U.S. Singh, Gurjeet Singh, Rohit Kumar Pachorkar, Padmakar Gupta, D.K. IES College of Technology Department of Mechanical Engineering Bhopal India Directorate General of Aeronautical Quality Assurance ORDAQA Kanpur India

ISBN: (纸本)9798350390728

robotics has been an integral part of manufacturing for decades, revolutionizing industries with automation and efficiency. As technology advances, the future of robotics in manufacturing holds tremendous promise, with innovations such as collaborative robots (cobots), artificial intelligence (AI), and advanced sensor technologies reshaping the landscape. This review paper provides an in-depth analysis of current trends, challenges, and prospects of robotics in manufacturing, exploring how these technologies will continue to transform production processes and enhance competitiveness in the global market. © 2024 IEEE.

关键词： Smart manufacturing

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Quality-Diversity Algorithms Can Provably Be Helpful for Optimization 33

Quality-Diversity Algorithms Can Provably Be Helpful for Opt...

引用

33rd international Joint conference on Artificial intelligence (IJCAI)

作者： Qian, Chao Xue, Ke Wang, Ren-Jian Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing 210023 Peoples R China

ISBN: (纸本)9781956792041

Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to find a set of high-performing, yet diverse solutions. They have found many successful applications in reinforcement learning and robotics, helping improve the robustness in complex environments. Furthermore, they often empirically find a better overall solution than traditional search algorithms which explicitly search for a single highest-performing solution. However, their theoretical analysis is far behind, leaving many fundamental questions unexplored. In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous runtime analysis. By comparing the popular QD algorithm MAP-Elites with (mu + 1)-EA (a typical EA focusing on finding better objective values only), we prove that on two NP-hard problem classes with wide applications, i.e., monotone approximately submodular maximization with a size constraint, and set cover, MAP-Elites can achieve the (asymptotically) optimal polynomial-time approximation ratio, while (mu + 1)-EA requires exponential expected time on some instances. This provides theoretical justification for that QD algorithms can be helpful for optimization, and discloses that the simultaneous search for highperforming solutions with diverse behaviors can provide stepping stones to good overall solutions and help avoid local optima.

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Exoskeleton Human-Computer Interaction Based on Deep Learning and sEMG Imaging 7

Exoskeleton Human-Computer Interaction Based on Deep Learnin...

引用

7th international conference on pattern Recognition and Artificial intelligence, PRAI 2024

作者： Xu, Wanni Guan, Minghua Fu, You-Lei Xie, Weifang Xiamen Academy of Arts and Design Fuzhou University Xiamen China School of Design and Fashion Zhejiang University of Science & Technology Zhejiang China College of Big Data and Internet Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798350350890

This research develops an optimized exoskeleton design for human-robot interaction, using surface electromyography (sEMG) to detect patient movement intentions and motor functions. The study involved rapid identification of human gait events, joint angles, and movements, using data from six individuals performing various movements on a 3D force platform. The exoskeleton and motion analysis system (MAS) processed this data, which was utilized for model training and validation. The evaluation was conducted using Root-mean-square error (RMSE) and Correlation Coefficient (CC). We introduced a novel sEMG analysis method leveraging deep and integrated learning to directly classify sEMG signals, achieving high classification accuracy (AUC of 0.9554). A fast-training classification technique utilizing motor-related cortical potentials and varied neural oscillations also exceeded the accuracy of existing classifiers, effectively supporting joint motion recognition and prediction. The optimized exoskeleton provides a viable and safe rehabilitation alternative for patients with neurological disabilities, offering a potential replacement for conventional therapies. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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Reinforcement Learning Control for a Flexible Joint Manipulator and Its Verification 6

Reinforcement Learning Control for a Flexible Joint Manipula...

引用

6th international conference on Mechatronics, robotics and Automation (ICMRA)

作者： Gao, HeJia He, ChuanFeng Hu, JuQi Wu, YiTong Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

ISBN: (纸本)9798350307542;9798350307535

(1)Flexible Joint Manipulator (FJM) is a system that can control its joints with high precision. This paper proposes a reinforcement learning (RL) method to control the FJM and achieve output constraint. The RL method can learn from the feedback of the system and adjust the control parameters accordingly. The paper also proves the stability of the system by using Lyapunov stability analysis. Finally, we verify the superiority of RL control method by comparative simulation.

关键词： Adaptive control reinforcement learning manipulator neural networks output constraints

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Design and analysis of a Hybrid Lower Limb Rehabilitation Robot Structure 4

Design and Analysis of a Hybrid Lower Limb Rehabilitation Ro...

引用

4th international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2024

作者： Liu, Binghua Liu, Fanghua Wang, Chen Lu, Junwei Jiangsu University of Science and Technology School of Mechanical Engineering Zhengjiang China

ISBN: (纸本)9798331531225

In response to the current issues with lower limb rehabilitation robots, a 3-degree-of-freedom hybrid lower limb exoskeleton has been designed. This exoskeleton consists of three parts: a waist device, a hip device, and a knee device. The waist structure can adaptively fit different body shapes. The hip device is a 3-UU mechanism that enables rehabilitation training for hip joint flexion/extension and adduction/abduction. The knee device is a URU-RRR parallel mechanism that facilitates rehabilitation training for knee joint flexion/extension. The modified Gruebler's formula was used to calculate the degrees of freedom of the mechanism, and the workspace cloud map of the mechanism was plotted using MATLAB. The driving function was input into Adams to obtain the end-effector motion trajectory. Finally, static analysis of key components was performed in Ansys Workbench to obtain the stress and deformation cloud maps of the mechanism. © 2024 IEEE.

关键词： MATLAB

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Design and analysis of Deployment Mechanism Based on Sliding Disc for Solid Surface Deployable Antenna 17th

Design and Analysis of Deployment Mechanism Based on Sliding...

引用

17th international conference on Intelligent robotics and Applications

作者： Li, Hao Ma, Xiaofei Zhang, Dayu Su, Guanlong Li, Huanxiao Yu, Zexing Xian Inst Space Radio Technol Xian 710100 Shaanxi Peoples R China

ISBN: (纸本)9789819607709;9789819607716

This paper designs a deployment mechanism based on sliding disc for solid surface deployable antenna. Firstly, a method for determining the folded petal posture based on particle swarm optimization algorithm is proposed, combined with the equivalent axis angle theorem to achieve the 1DOF deployment requirement. The deployment mechanism of solid surface deployable antenna is abstracted as a single closed-loop parallel platform. Based on the calculation of spatial mechanism degrees of freedom, the configuration synthesis of this type of antenna is established, and 136 feasible configurations are topologically identified. A detailed design analysis is conducted using the PUSR mechanism as an example. A kinematic model of the PUSR mechanism is constructed based on the closed vector method. The analysis results showed that the deployment process is stable and reliable, verifying the feasibility of the design scheme. Meanwhile, the analysis methods and conclusions can provide technical references for the design of solid surface deployable antennas.

关键词： Solid Surface Deployable Antenna Configuration Synthesis Kinematic analysis

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Research on Motion Control Technology of Quadruped Robot Arm Based on Visual analysis 6

Research on Motion Control Technology of Quadruped Robot Arm...

引用

6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Yao, Shuaibing Li, Danyong Zhang, Tong Ma, Mingxuan School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331541699

To address the issues of limited working range, weak interactivity, and low safety in robotic arm, a collaborative quadruped robot with an attached robotic arm is designed and implemented. Using the door-opening task as an example, this system covers target recognition and measurement, kinematic modeling and path planning, collision detection, and collaborative control. The Mask-RCNN algorithm is employed for precise identification of the door handle, and a depth camera is used to measure the spatial distance to the door handle. A torque observer based on generalized momentum is designed for collision detection of the robotic arm. Based on the coordinates of the target object and the observed external torque, a motion control strategy for the robotic arm to open the door is planned. Finally, a collaborative control logic is established between the quadruped robot and the robotic arm, enabling them to work together to complete the door-opening task while ensuring safety. © 2024 IEEE.

关键词： Multipurpose robots

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Deep Evidential Uncertainty Estimation for Semantic Segmentation under Out-Of-Distribution Obstacles

Deep Evidential Uncertainty Estimation for Semantic Segmenta...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Ancha, Siddharth Osteen, Philip R. Roy, Nicholas MIT Comp Sci & Artificial Intelligence Lab CSAIL Cambridge MA 02139 USA DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9798350384581;9798350384574

In order to navigate safely and reliably in novel environments, robots must estimate perceptual uncertainty when confronted with out-of-distribution (OOD) obstacles not seen in training data. We present a method to accurately estimate pixel-wise uncertainty in semantic segmentation without requiring real or synthetic OOD examples at training time. From a shared per-pixel latent feature representation, a classification network predicts a categorical distribution over semantic labels, while a normalizing flow estimates the probability density of features under the training distribution. The label distribution and density estimates are combined in a Dirichlet-based evidential uncertainty framework that efficiently computes epistemic and aleatoric uncertainty in a single neural network forward pass. Our method is enabled by three key contributions. First, we simplify the problem of learning a transformation to the training data density by starting from a fitted Gaussian mixture model instead of the conventional standard normal distribution. Second, we learn a richer and more expressive latent pixel representation to aid OOD detection by training a decoder to reconstruct input image patches. Third, we perform theoretical analysis of the loss function used in the evidential uncertainty framework and propose a principled objective that more accurately balances training the classification and density estimation networks. We demonstrate the accuracy of our uncertainty estimation approach under long-tail OOD obstacle classes for semantic segmentation in both off-road and urban driving environments.

关键词： Semantic Segmentation

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Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts 9

Ready or Not? A Robot-Assisted Crop Harvest Solution in Smar...

引用

IEEE international conference on Smart Computing (SMARTCOMP)

作者： Nguyen, Thai Thao Parron, Jesse Obidat, Omar Tuininga, Amy R. Wang, Weitian Montclair State Univ Dept Comp Sci Montclair NJ 07043 USA Montclair State Univ PSEG Inst Sustainabil Studies Montclair NJ 07043 USA

ISBN: (纸本)9798350322811

As robotics and artificial intelligence (AI) technologies have become increasingly relevant over the past couple of years, they will inevitably be key components for industries of all aspects which continue to expand to technological solutions. Particularly, the agricultural industry has progressed to using such means to minimize human involvement and reduce tasks that are time-consuming and costly. Motivated by this, we developed a robot-assisted crop maturity recognition and harvest system to accurately classify and detect the stages of ripeness the crops are in-ripe, medium ripe, and not ripe. Our proposed approach integrates computer vision, image processing, collaborative robotics, and a subcategory of artificial intelligence-transfer learning. The transfer learning-based model is trained to classify and recognize the crop in its maturity stages and locate the crop during real-time detection. Experimental results and analysis in real-world robot-assisted smart agriculture environments successfully demonstrated crop ripeness recognition accuracy, proving transfer learning could be utilized to effectively improve the efficiency and productivity of harvesting processes in the agricultural industry. The future work of this study is also discussed.

关键词： Collaborative robotics artificial intelligence smart agriculture transfer learning image processing computer vision

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