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检索条件"机构=School of Robotics and Automation Engineering"

共 2455 条记录，以下是61-70 订阅

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Demonstration Shaped Reward Machine for Robot Assembly Reinforcement Learning Tasks 16th

Demonstration Shaped Reward Machine for Robot Assembly Rein...

引用

16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Xiao, Ruihong Zhan, Hong Jiang, Yiming Yang, Chenguang School of Automation Science and Engineering South China University of Technology Guangzhou China School of Robotics Hunan University Changsha China

ISBN: (纸本)9789819964949

Reinforcement Learning has been proven to be successfully applied in robotic manipulation tasks, but it has been confronted with problems about interacting with the dynamic environment and generating high-level policies. Existing reward functions include sparse and shaped reward, and other way to view the reward functions as black boxes and then discover the best strategies by interacting with the environment. In this paper, a reward machine with demonstration definition for robot assembly is presented, which is a finite state machine that supports generating the specified reward function from the demonstration data and displays the reward function structure. By dividing the task demonstration data, extracting the key points of the task and defining the state of the robot, a reward function based on state migration is generated. The robot chooses the best reward according to the current state, which is conducive to using the reward structure to improve the sample efficiency and the quality of the result strategy. In this paper, we focus on the robot assembly reinforcement learning task. The structure of the proposed method is used to carry out experiments in the robot simulator. We compare the design methods with different reward functions and it proves that the proposed method can effectively improve the training efficiency of robot reinforcement learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Reinforcement learning

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Point Set Registration Based on Adaptive Maximum Correntropy 25

Point Set Registration Based on Adaptive Maximum Correntropy

引用

25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Shao, Yinfeng Cui, Wenting Xu, Liang Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350366600

Aiming at the problem that the iterative closest point algorithm has a lot of noise in data and poor robustness to outliers, this paper proposes a point set registration algorithm based on the adaptive maximum correntropy criterion. The aim is to enhance the robustness of traditional registration methods, improve registration accuracy, and reduce registration time. Firstly, the maximum correntropy criterion is introduced to effectively identify and eliminate noise and outliers in the data. Secondly, a parameter-adaptive iterative approach is proposed to iteratively remove noise and outliers in the data, thus improving the accuracy and robustness of point set registration. Experimental results on datasets containing a large amount of noise and outliers demonstrate that the proposed algorithm effectively enhances the accuracy and robustness of point set registration. © 2024 IEEE.

关键词： Adaptive Iterative closest point algorithm Maximum correntropy criterion Robustness

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Research on 3D Model Retrieval Technology Based on Audio Recognition 1

引用

13th International Workshop of Advanced Manufacturing and automation, IWAMA 2023

作者： Zhou, Chuanhong Wu, Junyang Chen, Kunpeng School of Automation and Mechanical Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200072 China

ISBN: (数字)9789819706655

ISBN: (纸本)9789819706648

At present, the number of 3D models in machinery industry is increasing, and the commonly used keyword retrieval methods need a lot of manual labeling work, and many complex 3D models are difficult to describe accurately in words. This paper proposes a 3D model retrieval algorithm based on audio recognition, which can quickly and effectively find 3D models in massive models. This paper takes the 3D model of STL file format as the research object, and its main work is as follows: Firstly, a new rotation normalization algorithm for 3D models is proposed. Then a point density control scheme of STL file based on unit volume is proposed. After that, an audio fingerprint extraction algorithm for 3D model is proposed. Finally,the original speed matching method and the neighborhood matching method are proposed to calculate the similarity between models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extraction

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Research on Wind Turbine Maintenance Based on Data-Physical Fusion Modeling 4

Research on Wind Turbine Maintenance Based on Data-Physical ...

引用

4th International Conference on Green Energy, Environment and Sustainable Development, GEESD 2023

作者： Wang, Weilong Cai, Hongxia Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781643684260

With the large-scale application of wind power generation technology, its maintenance and repair have received extensive attention. Since wind turbines are complex equipment coupled with multiple fields, the faults are sudden and diverse, and the fault samples are scattered. At the same time, wind turbine operation data has non-linear and unstable time-series characteristics. Single data-driven and physics-driven methods can no longer well meet the current wind turbine maintenance needs. This paper proposes a wind turbine maintenance method with data-mechanism fusion modeling. A data-driven algorithm considering the timeseries characteristics of wind turbine operation data is designed. The fusion strategy for updating the boundary parameters of the machine physical model is proposed. Simulation calculations are completed using a fluid-solid coupling model. Simulation results optimize the wind turbine maintenance strategy to guide wind farm maintenance activities. © 2023 IOS Press BV. All rights reserved.

关键词： Wind turbines

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Research on Structural Topology Optimization Based on SIMP-BESO Coupling Algorithm 1

引用

13th International Workshop of Advanced Manufacturing and automation, IWAMA 2023

作者： Zhou, Chuanhong Sun, Yi Tan, Youquan School of Automation and Mechanical Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200072 China

ISBN: (数字)9789819706655

ISBN: (纸本)9789819706648

Structure topology optimization technology plays an increasingly important role in mechanical engineering, as it allows high quality structures to be obtained while meeting the demands for lightweight structures. Using topology optimization makes it possible to distribute the material in a given area appropriately to minimize the weight of the structure while meeting the performance requirements. And it reduces the structural design cycle and gives engineers more structural options. This paper takes the SIMP algorithm as the object of study and proposes a SIMP-BESO coupling algorithm for solving the grayscale problem in the algorithm. This algorithm is based on the concept of the termination factor for SIMP, linking the optimization process to the number of iterations. Dynamic movement factor makes the iterative process "fast then slow" to achieve the phenomenon of removing the grayscale elements in structure, and structures with smaller flexibility and larger stiffness can be obtained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Shape optimization

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Resistive Micro/Nano Flexible Sensitive Sensing Based on Silver Nanowires and Polydimethylsiloxane Layering 9

Resistive Micro/Nano Flexible Sensitive Sensing Based on Sil...

引用

9th International Conference on Mechanical engineering, Materials, and automation Technology, MMEAT 2023

作者： Sun, Weiming Liu, Mei Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering Automation Shanghai University 200444 China

ISBN: (纸本)9781510668584

A flexible sensor refers to a type of sensor that possesses the ability to be bent and stretched. Typically composed of soft materials, these sensors demonstrate high deformability and adaptability. They are capable of conforming to complex shapes and surfaces, enabling the measurement of various physical quantities. The application prospects of flexible sensors are vast, encompassing fields such as biomedical sensing, health monitoring, virtual reality, and intelligent wearable devices. Micro and nano flexible sensors are commonly fabricated using techniques like inkjet printing or screen printing. However, these methods often necessitate the incorporation of sensitive materials into viscous substances, resulting in reduced sensor sensitivity. In order to concurrently meet the requirements for conductivity and structural integrity of the sensor's sensitive materials, a novel micro/nano flexible sensor was developed employing silver nanowires (AgNWs) and polydimethylsiloxane (PDMS). By selectively filtering silver nanowires, sensitive films are formed, and the structure of the sensitive materials is achieved by patterning the PDMS substrate. This approach effectively mitigates lateral effects while ensuring sensor sensitivity. The sensor exhibits the capability to detect deformations ranging from 0~5%, with a minimum resolution of 0.1N for pressure detection. Additionally, it displays unidirectional strain sensitivity and possesses self-recovery characteristics. © 2023 SPIE.

关键词： Polydimethylsiloxane

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Driver Intention Prediction Using Text Prompts with In-Cabin and Out-Cabin Cameras 16

Driver Intention Prediction Using Text Prompts with In-Cabin...

引用

16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： He, Bin Yu, Ningmei Han, Hongcheng Xi'an University of Technology School of Automation and Information Engineering Xi'an China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China

ISBN: (纸本)9798331506896

Human-machine collaborative driving is poised to become a cornerstone of intelligent transportation systems, where accurate recognition of driver behavior and intent is crucial for vehicle decision-making and real-time traffic management. Existing methods for driver maneuver recognition, primarily based on 3D convolutional neural networks (3D-CNNs) and Transformers, encounter challenges such as spatiotemporal redundancy, limited spatial perception, and inadequate capture of detailed object motion. To overcome these limitations, we propose a novel text-driven approach utilizing Large Language Models (LLMs) for driver maneuver recognition. By integrating textual semantics from both in-car and external environments, the Contrastive Language-Image Pre-training (CLIP) text encoder enhances the model's comprehension of complex video scenarios, improving both training and inference efficiency while enabling more precise identification of motion dynamics. Experimental results demonstrate that our approach outperforms state-of-the-art methods in driver intention prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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A Multi-Class Imbalanced Learning Method Using Random Balanced Sampling for Predicting the Quality Level of Diesel Engines

A Multi-Class Imbalanced Learning Method Using Random Balanc...

引用

2024 International Conference on automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Song, Yi-Tian Sun, Yan-Ning Zhu, Jia-Yang Liu, Li-Lan Gao, Zeng-Gui Zhang, Jia-Hao School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (纸本)9798350378658

Numerous process items and procedures in the assembly process of a diesel engine have a direct and significant impact on its performance, and accurate and effective assembly process control is a key technical bottleneck in improving the quality consistency of batch-produced diesel engines. Accurate prediction of assembly quality can provide support for online control of assembly quality. However, due to the significant class imbalance between qualified and unqualified products of diesel engine assembly quality, the traditional imbalance learning makes it difficult to capture the small changes between classes. Therefore, an improved random balance sampling method is proposed, combined with the LightGBM classifier to create a quality prediction model. Firstly, class balancing of the training dataset is achieved by generating random weights for each class and fusing oversampling and undersampling. Then, a LightGBM-based quality prediction model is built. Finally, the effectiveness of this approach is validated using real industrial diesel engine assembly data. The comparison results demonstrate that the proposed method outperforms traditional resampling techniques. This study provides an accurate quality prediction model, laying the groundwork for the assembly quality control model of diesel engines. © 2024 IEEE.

关键词： Diesel engines

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An Improved Plant Disease Detection Method Based on YOLOv5 15

An Improved Plant Disease Detection Method Based on YOLOv5

引用

15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Cai, Hongxia Jiang, Jiaying School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China

ISBN: (纸本)9798350326178

To overcome the challenges of weak generalization, high false detections, and missed targets in multi-scale plant disease detection under complex scenarios, this paper proposes an enhanced method based on YOLOv5. The method improves YOLOv5 by incorporating a multi-scale feature fusion module, an attention mechanism, and optimizing the loss function to accurately locate and classify plant disease targets. Experimental results show significant improvements over the original YOLOv5 model in terms of mAP, precision, and recall, with enhancements of 3.99%, 1.56%, and 1.27% respectively. Additionally, the proposed method outperforms other commonly used models. This study provides an efficient and accurate approach for plant disease detection, enabling real-time monitoring of plant health and precise disease control in plant factories. © 2023 IEEE.

关键词： Object detection

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Offline Deep Reinforcement Learning Two-stage Optimization Framework Applied to Recommendation Systems 43

Offline Deep Reinforcement Learning Two-stage Optimization F...

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43rd Chinese Control Conference, CCC 2024

作者： Jiang, Yihua Liu, Qi Li, Yanjie the Harbin Institute of Technology Shenzhen Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation 518055 China

ISBN: (纸本)9789887581581

Most traditional recommendation systems (RS) improve the model structure to obtain valid user behavior representation and make better recommendations. However, limited by the scale of the model, the model cannot accommodate an extended range of user historical data and cannot effectively extract long-term preferences. Reinforcement learning (RL) aims to maximize the cumulative return. Introducing RL can smoothly solve the long-term and short-term profit balance. However, there are specific problems with the direct combination of the two. Training RS that relies on offline user data will cause the strategy to suffer from an overestimation of action and distribution shift. Unlike the traditional RL environment, the ample space of the RS will also cause problems of insufficient exploration and sparse rewards, making the policy more difficult to learn. In response to the above issues, this work proposes an offline RL two-stage optimization framework. The framework consists of an offline RL method based on unknown behavior to alleviate the problem of action overestimation and a two-stage feature optimization training method to reduce the impact of large spaces. Experimental results prove that the method proposed in this article can effectively improve the long-term recommendation ability of the model and more effectively improve the model performance than the general offline RL algorithm. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Deep reinforcement learning

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