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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Tian, Yujie Guang, Chengfang Luo, Ruiqing Bao, Sheng Yuan, Jianjun Du, Liang Hu, Zhengtao School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China Shanghai Robotics Institute School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781665481090

Accurate contact force estimation is crucial for interactions between the robot arm and its environment. Presently, contact force estimation based on current information often suffers from biases caused by unmodeled errors, noise, and various other factors. In contrast, tactile sensors information effectively complements current information, enhancing the estimation of contact forces in robot arm interactions. To improve the accuracy of force estimation without force/torque sensors, we propose an information fusion method utilizing Kalman Filter (KF) to incorporate motor current with the tactile sensor for a nursing robot arm with Differential Modular Joints (DMJ). The coupling dynamic model of the DMJ and the contact force estimation model of the tactile sensor are established respectively. Subsequently, a linear KF fusion method is introduced based on these two methods. The experimental results indicate that contact force can be reliably estimated without force/torque sensors, significantly enhancing the accuracy of force estimation. © 2024 IEEE.

关键词： Robotic arms

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Semantic Map Construction Method Based on Brain-Inspired SLAM 14

Semantic Map Construction Method Based on Brain-Inspired SLA...

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14th IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Zhao, Hangpiao Tang, Fengzhen Shenyang Institute of Automation The State Key Laboratory of Robotics Shenyang China The University of Chinese Academy of Sciences Shenyang Institute of Automation Beijing China

ISBN: (纸本)9798331506056

Simultaneous localization and mapping (SLAM) is one of the core techniques for robots to move autonomously in unknown environments. Rat-SLAM, inspired by the navigation mechanism of the animal brain, provided nice performance in many environments. However, the experience map constructed by RatSLAM excluded semantic information, which is considered to play an important role in human environmental cognition and spatial navigation. In this paper, we propose a method to construct semantic experience map, combining the state-of-the-art deep neural network and RatSLAM. In our method, YOLOv5 is utilized to extract semantic information from the environmental images. The information of static objects, such as road signs, is selected to be incorporated into the experience map, being more closely to the brain navigation. To avoid the same object being repeatedly marked, we propose a semantic dynamic island mechanism, which only marks the center of the island in the experience map. The proposed method is verified on KITTI datasets, showing that it can successfully realize the construction of semantic experience map. The semantic experience maps are more intuitive and understandable by human, facilitating human-robot interaction, i. e. helping robots to fulfil more complex tasks for human. © 2024 IEEE.

关键词： Rats

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Boosting Behavior Tree Generation for Robots with Large Language Models and Genetic Programming

Boosting Behavior Tree Generation for Robots with Large Lang...

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2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Verdaguer-Gonzalez, Aaron Dalmau-Moreno, Magi Merino, Luis Garcia, Nestor Eurecat Technology Centre of Catalonia Robotics and Automation Unit Barcelona Spain Universidad Pablo Olavide Service Robotics Laboratory Sevilla Spain

ISBN: (纸本)9798331516857

Mobile robots are becoming increasingly ubiquitous in modern society, requiring more human-like interaction capabilities, such as following operator instructions and collaborating with humans. Conventional robot programming methods often fall short in achieving these complex behaviors. Behavior Trees (BTs) offer a promising alternative due to their modularity, scalability and reactivity. We propose utilizing general purpose, system-prompted Large Language Model (LLM) assistants to decompose task descriptions into executable BTs, which are subsequently refined using Genetic Programming (GP) and a state machine like low-resource BT execution simulator, where they will be tested for task completion. This approach eliminates the need for fine-tuning LLMs, thereby reducing computational costs and saving time and energy. Our method successfully solves all proposed scenarios, enhances applicability across diverse environments, and democratizes behavior generation for non-experts, outperforming baseline methods in efficiency. © 2025 IEEE.

关键词： Mobile robots

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Point-line RGB-D SLAM Algorithm Based on Multiple Back-projection Strategy 7

Point-line RGB-D SLAM Algorithm Based on Multiple Back-proje...

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7th International Conference on robotics, Control and automation Engineering, RCAE 2024

作者： Sun, Xianshuai Wang, Yabiao Zhao, Yuming Li, Zhigang He, Zhen Wang, Xiaohui Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Key Laboratory of Marine Robotics Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350355642

To overcome the limitations of the application of the SLAM algorithm, which is based on point features under harsh conditions, this paper innovatively proposes a SLAM algorithm that is based on point and line feature fusion. The algorithm uses an RGB-D camera as an input sensor to extract feature points and feature lines simultaneously from the input RGB image. To fully exploit spatial line feature information and mitigate the impact of environmental noise, we propose a multiple back-projection strategy guided by a depth map to robustly recover spatial line information. Finally, the proposed algorithm is compared with the ORB-SLAM2 algorithm in the public dataset TUM RGB-D. The results show that the proposed algorithm significantly improves the accuracy of localization and mapping. © 2024 IEEE.

关键词： Mapping

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Performance prediction method of solid rocket motor based on the ground test 4

Performance prediction method of solid rocket motor based on...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Hu, Xiaomei Hua, Yuting Weng, Jiahong Teng, Xianshu Zhang, Mingnan The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200072 China

Solid rocket motor (SRM) is a core component of rockets. Considering the high cost of solid rocket motors and the high demand for transportation and storage, there is an urgent need to predict the performance of solid rocket motors before the ground ignition test to lower the cost of tests. Using data mining technology to predict various parameters of rocket motors with historical data has become a new development direction. In order to improve the data fitting effect and prediction accuracy, a novel prediction model was proposed, which tries to combine the Whale Optimization Algorithm (WOA) with the Error Correction Gated Recurrent Unit neural network (ECGRU) to correct the basic prediction performance of the Long Short-Term Memory neural network model (LSTM). Compared with traditional methods, experiments showed that the novel model was beneficial in improving the reliability of the prediction of the thrust and specific impulse. © Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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Motor test data quality evaluation model based on critic-fluctuant weight assignment method 3

Motor test data quality evaluation model based on critic-flu...

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2024 3rd International Conference on Electrical, Control and Information Technology, ECITech 2024

作者： Hu, Xiaomei Teng, Xianshu Hua, Yuting Weng, Jiahong The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200072 China

The article addresses the limitations of traditional weight analysis methods in handling ground ignition experiment data for solid rocket motors, particularly the anti-interference ability to noise factors. To address this issue, the specialized Critic-Fluctuant weight analysis method is proposed. This method calculates objective weight values based on fluctuation values and integrates noise standard scores into data quality assessment, enabling researchers to effectively and intuitively identify different levels of data quality. After comparing and analyzing the objective weights and data quality scores of 100 ground ignition test data sets, it is verified that Critic-Fluctuant has higher adaptability and accuracy than traditional CRITIC methods in ground ignition test scenarios. The article provides new insights and tools for researchers to deal with similar data characteristics. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Quality control

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Spatial Data Thickening-Based Carbon Nephogram Using UAV Real-Time Monitoring

Spatial Data Thickening-Based Carbon Nephogram Using UAV Rea...

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International Conference on Mechanical Design, ICMD 2023

作者： Nie, Zhiqi He, Bin Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9789819709212

This study explores the combination of unmanned aerial vehicle (UAV) and ground monitoring for real-time carbon footprint mapping technology in low-carbon smart parks. Addressing the conflict between sampling rate and monitoring efficiency in UAV monitoring, we propose a spatial data thickening algorithm based on moving window spline interpolation and time series fusion with inverse distance weighting. Meanwhile, a comprehensive carbon monitoring algorithm is proposed by combining ground and spatial carbon emission calculation methods. Furthermore, the concept of carbon nephogram is introduced to depict the spatial distribution and intensity of carbon emissions. By establishing an integrated monitoring system and conducting simulation experiments in a small-scale smart park scenario, the results show that the collaborative monitoring system has superiority in improving monitoring efficiency and affording intuitive insights. It provides an effective methodology for the intelligent design and maintenance of smart parks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Carbon footprint

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Ensemble-based Offline Data-driven Evolutionary Optimization for Operation Parameter of Blast Furnace 20

Ensemble-based Offline Data-driven Evolutionary Optimization...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ye, Qiucheng Cai, Hongxia Bian, Yunqi School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (纸本)9798350356328

Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it is difficult to perform accurate modeling and simulation experiments for a blast furnace. This poses great difficulties in performing the evaluation of control strategies in real-time. Offline evolutionary optimization, as an optimization technique that relies only on historical data, enables the search for optimal operation parameters through the introduction of surrogates. Based on the characteristics of historical time-series data of the iron-making process, this study proposes an offline evolutionary optimization method based on advanced time-series surrogates. Ensemble strategy combining surrogates with different prediction time windows contributes robustness during the optimization process. Additionally, multiple sub-regions are maintained to enable extensive searching of the decision space with a multi-objective nature. The efficiency of the proposed pooling method is demonstrated through verification in actual production cases. © 2024 IEEE.

关键词： Blast furnaces

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Biosyncretic robotic systems:Comprehensive construction,application,and future challenges

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Science China(Technological Sciences) 2025年第4期68卷 1-23页

作者： Qi ZHANG Chuang ZHANG Lianchao YANG Hengshen QIN Yiwei ZHANG Ying ZHAO Lianqing LIU State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China University of Chinese Academy of Sciences Beijing 100049China Department of General Surgery Shengjing Hospital of China Medical UniversityShenyang 117004China

Owing to the deep integration of biological and electromechanical systems,biosyncretic robotic systems have emerged as a significant research interest in *** study presents the latest related research featuring various biological *** the classification of biosyncretic robotic systems,the key technologies for construction are elucidated and summarized,including comprehensive structural design,fabrication,and behavior control ***,emphasis has been placed on their applications,particularly within the biomedical ***,the challenges are summarized,and their future developments are envisioned.

关键词： biosyncretic robots biohybrid robots bioactuators cell actuation living machines

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MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted Industrial Wireless Networks

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Computers, Materials & Continua 2025年第2期82卷 3203-3222页

作者： Ru Hao Chi Xu Jing Liu College of Information Engineering Shenyang University of Chemical TechnologyShenyang110142China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China

Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.

关键词： Industrial wireless networks(IWNs) multi-access edge computing(MEC) non-orthogonal multiple access(NOMA) task offloading resource allocation

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