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检索条件"任意字段=2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023"

共 115 条记录，以下是31-40 订阅

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Research on Dynamic Disassembly Line Balance of mixed model with Uncertain Product State

Research on Dynamic Disassembly Line Balance of mixed model ...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Huang, Wanfeng Gu, Xin Wuhan University of Technology School of Information Engineering Wuhan China Hubei Standardization and Quality Institution Wuhan China

ISBN: (纸本)9798350301113

In this paper, the uncertainty of product state is introduced. How to balance the dynamic disassembly line under the condition of uncertain product state is a problem that has not been studied yet. This is also the main research content of this paper. Taking minimizing cycle time and peak energy consumption as objective functions, a mathematical model of dynamic disassembly line balance based on the above-mentioned problem is established. On this basis, a dynamic disassembly line balance planning algorithm based on tracking moving optimization is proposed to verify the effectiveness of the model and compare the performance of several classical multi-objective optimization algorithms on the problems raised in this paper. In order to solve the problem of frequent switching of the optimal solution obtained by tracking moving optimization, a robust definition based on constraints and a dynamic disassembly line balance planning algorithm based on robust optimization over time are further proposed. The effectiveness of the proposed algorithm is verified by experiments on several evaluation indexes. © 2023 IEEE.

关键词： Energy utilization

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Intelligent Java Dataset Construction and Visualization Evaluation for Reliable software Development

Intelligent Java Dataset Construction and Visualization Eval...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Li, Dian Wang, Weidong Zhao, Yang Beijing University of Technology Faculty of Information Technology Department of Software Engineering Beijing China

ISBN: (纸本)9798350301113

As big data and artificial intelligence continue to gain importance, the reliable quality of datasets has be-come a crucial factor in algorithm performance and result reliability. However, many datasets lack standardization and quality control, which can lead to potential issues in software development and data analysis. In this paper, we propose an objective dataset evaluation algorithm that utilizes multiple metrics, including unified naming conventions and document annotations, for statistical analysis and filtering to ensure reliable data. Our approach scores datasets using reasonable criteria, and we use visualization techniques inspired by remote sensing to compare and visualize reliable dataset quality. Our research demonstrates that our evaluation method is feasible and can assist software developers in enhancing the reliable quality and efficiency of software development by improving dataset quality. Our study concentrates on datasets obtained from GitHub using a web crawler, and our approach establishes a standardized technique for evaluating reliable dataset quality. © 2023 IEEE.

关键词： Python

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A YOLOV5-PF-based Approach to Visitor's Interest Discrimination applied to Pavilion Tour-guide Robots

A YOLOV5-PF-based Approach to Visitor's Interest Discriminat...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Liu, Xuekai Meng, Xiangyin Xiao, Shide Li, Ying Southwest Jiaotong University School of Mechanical Engineering Chengdu610031 China

ISBN: (纸本)9798350301113

In the field of human-computer interaction, accurate proactive interaction behaviors by robots can effectively attract user attention and enhance user experience. In this work, a method called the visitor's interest discrimination method is proposed for exhibition and museum guide robots to identify visitors in the environment who are suitable for initiating proactive interactions before initiating human-computer interaction requests. The proposed method analyzes the bounding boxes of targets based on YOLOv5-PF (YOLOv5-Poo1Former) object detection and combines a weight analysis algorithm that integrates the interaction area and interaction distance between visitor and showcases. This enables the estimation of visitor attention towards the showcases and inferring visitors' interests in the showcases. Through experimental validation, the proposed method accurately discriminates visitors' interests in the environment. © 2023 IEEE.

关键词： Human computer interaction

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A Generalized Learning Method for Obstacle Constraint Tasks Based on Dynamic Movement Primitives

A Generalized Learning Method for Obstacle Constraint Tasks ...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Niu, Hao Zhang, Xiuli Beijing Jiaotong University School of Mechanical Electronic and Control Engineering Beijing China

ISBN: (纸本)9798350301113

To address the problem of high randomness and uncertainty in the learning of a single trajectory of Dynamic Movement Primitives (DMP), which cannot satisfy the corresponding kinematic constraints, we propose a generalized learning method for DMP under constrained tasks. The first stage is the model building and learning phase. For the acquisition of a demonstration trajectory reflecting the movement characteristics of the operational task, the demonstration trajectory is processed using a Gaussian mixture model and Gaussian mixture regression, a statistical-based learning method, to acquire multiple trajectory distribution characteristics. The second stage uses an optimization algorithm for the trajectory. By introducing kinematic constraints based on DMP, while treating obstacles as constraints, a trajectory is obtained that satisfies the kinematic constraints for obstacle avoidance. To evaluate the performance of our proposed algorithm, trajectory avoidance simulations are conducted. The results show that the generated trajectory can perform the obstacle avoidance task and satisfy the desired kinematic constraints. © 2023 IEEE.

关键词： Trajectories

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EFNQL: An Efficient Method Based On Q-learning And Fuzzy Neural Networks In Robocup Soccer Simulation

EFNQL: An Efficient Method Based On Q-learning And Fuzzy Neu...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Wang, Lepeng Tan, Zhiqiu Liu, Jiangyi Yang, Yinze Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350301113

Robocup Soccer Simulation is considered to be one of the world's most prestigious and large-scale robotics events, attracting researchers from all over the world. In Robocup Soccer Simulation 2D, the challenge has always been to organize intelligent agents to collaborate and analyze scenarios in order to develop more effective strategies for launching shots and dribbling. To address this challenge, an efficient algorithmic architecture called EFNQL has been developed that utilizes fuzzy control, fuzzy neural networks, and Q-learning to optimize the strategy of the agents in Robocup Soccer Simulation competitions. The architecture first establishes fuzzy rules using fuzzy control theory, then processes more complex information on the playing field using fuzzy neural networks, and finally completes fuzzy reasoning by applying Q-learning related theory for learning. Through extensive experimentation, this thesis found that the scoring rate and ball control rate were significantly improved, ultimately resulting in a second-place finish at the 2022 RoboCup China Open. © 2023 IEEE.

关键词： Fuzzy neural networks

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Trans-ARPG: Automated ICD Coding method based on Adversarial Reinforcement Path Generation and Transformer mechanism

Trans-ARPG: Automated ICD Coding method based on Adversarial...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Zhang, Yunzhu Zhang, Jin Liu, Xianfeng Hunan Normal University College of Information Science and Engineering Chang Sha China

ISBN: (纸本)9798350301113

The international Classification of Diseases (ICD) is a vital tool used in clinical and health management, providing codes for disease classification. The use of deep learning techniques to automatically extract valuable information from medical records and assist in coding has gained significant attention due to the increasing volume of medical data and the advancement of precision medicine research. However, current coding methods face challenges such as the large candidate space for disease coding and imbalanced code distribution. This study focuses on these challenges and proposes a hierarchical ICD automatic coding method. By introducing a Transformer-based hierarchical path propagation mechanism, the study effectively captures the relationships between disease codes at different hierarchical levels and reduces the candidate space for coding. Experimental results demonstrate the method's efficacy in information extraction and coding improvement. © 2023 IEEE.

关键词： Generative adversarial networks

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Multi-source Transfer-assisted Evolutionary Optimization for Dynamic Human-Robot Collaborative Disassembly Planning

Multi-source Transfer-assisted Evolutionary Optimization for...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Fang, Yilin Tu, Jin Wuhan University of Technology School of Information Engineering Wuhan China

ISBN: (纸本)9798350301113

Traditional manual disassembly can no longer meet the needs of modern enterprises, so more industrial robots are designed to assist or even replace humans to complete the disassembly. In actual disassembly environments, there are many uncertainties that affect the whole disassembly process. In this paper, a dynamic optimization problem of human and robot collaborative disassembly line balancing is studied, which is based on the uncertainty of human ability. Moreover, the dynamic optimization problem needs to follow the Pareto optimal solution set quickly and accurately, and transfer learning has been proved to be a suitable method. Therefore, this paper proposes a multi-source transfer-assisted evolutionary dynamic optimization algorithm. The algorithm reuses knowledge from multiple historical environments to accelerate the generation of initial population and improve the convergence speed of the solution. In the end, based on several sets of problem instances with different scales and environmental similarities, the effectiveness of the algorithm is validated through comparison of several competitors. © 2023 IEEE.

关键词： Industrial robots

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Research on SaaS ERP service provider selection model of small and medium-sized enterprises based on ELECTRE-IV

Research on SaaS ERP service provider selection model of sma...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Shang, Yanying Jiang, Junfeng Zhang, Ruochen Xi'an University of Technology School of Economics and Management Xi'an China Nanjing University of Information Science and Technology School of Management Science and Engineering Nanjing China

ISBN: (纸本)9798350301113

The choice of SaaS ERP service provider is the key for SMEs to successfully implement ERP projects. This paper aims to select the most suitable SaaS ERP service provider for enterprises and empirically analyses the influencing factors of SaaS ERP service provider selection through the two dimensions of the enterprise's factors and SaaS ERP service provider factors. The influencing factors are determined as follows: SaaS ERP service provider strength, enterprise operation capability, SaaS ERP service platform configuration, and enterprise compatibility. Based on this, the index system of SaaS ERP service provider selection is determined. Aiming at the characteristics of multi-objective attributes and uncertainty, the fuzzy ELECTR-IV method is applied to establish the SaaS ERP service provider selection model of SME based on the 'enterprise-service provider' matching perspective, and the case verification is carried out to guide the SaaS ERP service provider selection of SMEs. © 2023 IEEE.

关键词： Enterprise resource planning

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Multi-fidelity optimization with Adaptive Optimal Sampling for Position-constrained Human-robot Collaborative Disassembly Sequence Planning and Line Balancing

Multi-fidelity optimization with Adaptive Optimal Sampling f...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Fang, Yilin Wang, Siwei Wuhan University of Technology Wuhan School of Information Engineering China

ISBN: (纸本)9798350301113

When disassembly large-occupied end-of-life (EOL) products, the operation position is constrained due to geometric constraints and distribution. Operators with multiple tasks need to switch between designated operation positions, and the moving time cannot be ignored in this case. This paper proposes a position-constrained human-robot collaborative disassembly sequence planning and line balancing (PC-HRC-DSPLB) problem. In PC-HRC-DSPLB, each task has an optional operation position set, and the opted task will be assigned to a selectable operation position so that it can be completed earlier. Considering the solving difficulty of PC-HRC-DSPLB, a multi-fidelity optimization with adaptive optimal sampling (MOAOS) is proposed to solve a great deal of PC-HRC-DSPLB instances. The group order and sampling probability are adjusted adaptively in MOAOS, and the effectiveness of MOAOS is proved by comparing with typical multi-fidelity optimization algorithms. © 2023 IEEE.

关键词： Constrained optimization

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Learning of Leg Stiffness and Damping to Match Robot Dynamics

Learning of Leg Stiffness and Damping to Match Robot Dynamic...

引用

2023 international conference on frontiers of robotics and software engineering, frse 2023

作者： Huang, Senwei Zhang, Xiuli Beijing Jiaotong University Bjtu School of Mechanical Electronic and Control Engineering Beijing China

ISBN: (纸本)9798350301113

The spring-loaded inverted pendulum model, a simplified robot dynamics model, is regularly employed for the purpose of controlling the motion of legged robots. It has two key parameters to be determined, namely, spring stiffness and spring damping. For different robot systems, model parameters need to be set to match the robot's dynamic characteristics. Nevertheless, it's hard to match the model parameters with the robot's dynamic characteristics through manual adjustment in engineering practice. Inspired by animals' hierarchical and modular motion control features, this paper designs a hierarchical motion control framework for robot dynamics matching. The spring stiffness and damping of the SLIP model are learned through the deep reinforcement learning algorithm to match the control model with the robot's dynamic characteristics. Finally, the performance of the hierarchical motion control framework is tested on a one-legged robot by using dynamics simulation software through comparative experiments. The simulation experimental results show that the robot with learned model parameters has a less mechanical cost of transport and better motion stability than the robot with fixed model parameters. © 2023 IEEE.

关键词： Reinforcement learning

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