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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Sayandip Paul C Hemanth Kumar Arunkumar Bongale Robotics and Automation Symbiosis Institute of Technology Pune India

The two main parts of an autonomous vehicle lane change maneuver system are a lane change planner and a lane change controller. This paper describes a model-based design strategy for developing such a system. This means that the lane change planner is in charge of creating a lane change trajectory that is both safe and efficient. The lane-changing controller is tasked with the actual execution of the trajectory, involving the management of the vehicle’s steering, acceleration, and braking. The lane change planner utilizes several strategies to craft trajectories that prioritize safety and efficiency. The planner starts with a time-to-collision analysis to determine which targets are safe or hazardous. If the present lane is considered safe, it then generates terminal states that are specific for the front automobile following modes. The lane changing maneuver system underwent evaluation within a simulation environment. The results of this assessment demonstrate the system’s capability to adeptly and securely execute lane change maneuvers across diverse driving scenarios.

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Video Streaming Protocols Comparison in Field Experiments for Aerial Teleoperation 9

Video Streaming Protocols Comparison in Field Experiments fo...

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9th International Conference on Mechanical Engineering and robotics Research, ICMERR 2025

作者： Fernandez, Manuel J. Franceschini, Riccardo Becerra, Julian Cayero Fumagalli, Matteo Technical University of Denmark Department of Electrical Engineering Automation and Control Kongens Lyngby2800 Denmark Robotics and Automation Unit Eurecat Technology Center of Catalunya Cerdanyola Del Vallès Barcelona Spain

ISBN: (纸本)9798331509262

As aerial robots evolve to perform complex tasks such as manipulation and visual inspection, maintaining the pilot at the center of decision-making is critical for mission safety and leveraging human expertise. A key enabler of this collaboration is reliable video streaming between the robot and the operator. This paper presents a comparative analysis of four video streaming protocols RTP (Real-time Transport Protocol), RTSP (Real-Time Streaming Protocol), SRT (Secure Reliable Transport), and ROSTCP (ROS Transport Control Protocol) based on extensive field experiments. The evaluation focuses on CPU usage, network throughput, latency, streaming quality, and packet integrity across varying distances. Tools such as iperf, ping, GStreamer, and Wireshark were employed for detailed performance measurements. Results reveal significant differences: SRT and RTP deliver superior network stability and resilience to packet loss, while RTSP and ROSTCP show advantages in CPU efficiency. These findings provide actionable insights for optimizing video streaming in aerial robotics, emphasizing the trade-offs and advantages of each protocol in real-world scenarios. © 2025 IEEE.

关键词： Social robots

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

Demonstration Shaped Reward Machine for Robot Assembly Rein...

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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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Full Resolution Repetition Counting 16th

Full Resolution Repetition Counting

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

作者： Li, Jianing Chen, Bowen Wang, Zhiyong Liu, Honghai School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819964826

Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions. To handle the various length of videos and repetitive actions, also optimization challenges in end-to-end video model training, down-sampling is commonly utilized in recent state-of-the-art methods, leading to ignorance of several repetitive samples. In this paper, we attempt to understand repetitive actions from a full temporal resolution view, by combining offline feature extraction and temporal convolution networks. The former step enables us to train repetition counting network without down-sampling while preserving all repetitions regardless of the video length and action frequency, and the later network models all frames in a flexible and dynamically expanding temporal receptive field to retrieve all repetitions with a global aspect. Besides, temporal self-similarity matrix is used in our model to represent the correlation of action, which contains much cycle information in time series. We experimentally demonstrate that our method achieves better or comparable performance in three public datasets, i.e., TransRAC, UCFRep and QUVA. We expect this work will encourage our community to think about the importance of full temporal resolution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Speed Fluctuation Suppression of Current Scaling Errors Based on Improved Iterative Learning Control Method 26

Speed Fluctuation Suppression of Current Scaling Errors Base...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Mu, Haoyi Yu, Guodong Li, Shaobin Zhu, Pengcheng Xu, Yongxiang School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9798350317589

In recent years, there has been a growing demand for high-precision speed control of permanent magnet synchronous motor (PMSM), drawing significant attention to the issue of inherent current measurement errors in drive control. This paper presents a novel approach to mitigate periodic speed fluctuations using an expert system-based iterative learning control (ILC) method. The proposed method employs an expert system to dynamically adjust the weight values of the memory factor during online updates. This ensures the calculated relaxation factor optimally suppresses speed fluctuations across all operating conditions. Furthermore, the stability and parameter tuning aspects are analyzed from the perspective of transfer functions, and the feasibility of the proposed method is demonstrated through simulation. © 2023 IEEE.

关键词： Errors

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How Collective Intelligence Emerges in a Crowd of People Through Learned Division of Labor: A Case Study

How Collective Intelligence Emerges in a Crowd of People Thr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Dekun Zhang, Hongwei School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Guangdong Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper investigates the factors fostering collective intelligence (CI) through a case study of *LinVi's Experiment, where over 2000 human players collectively controll an avatar car. By conducting theoretical analysis and replicating observed behaviors through numerical simulations, we demonstrate how self-organized division of labor (DOL) among individuals fosters the emergence of CI and identify two essential conditions fostering CI by formulating this problem into a stability problem of a Markov Jump Linear System (MJLS). These conditions, independent of external stimulus, emphasize the importance of both elite and common players in fostering CI. Additionally, we propose an index for emergence of CI and a distributed method for estimating joint actions, enabling individuals to learn their optimal social roles without global action information of the whole crowd. © 2024 IEEE.

关键词： Linear systems

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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Early Detection and Treatment of Skin Diseases Using Advanced Image Preprocessing and Machine Learning Techniques 4

Early Detection and Treatment of Skin Diseases Using Advance...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Pandian, Jeswin Paul Jegan, R. Jerlinkasmir, R. Jose, P. Subha Hency Raj, P. Anantha Christu Karunya Institute of Technology and Science Division of Biomedical Engineering Coimbatore India Karunya Institute of Technology and Science Department of Mathematics Coimbatore India Karunya Institute of Technology and Science Division of Robotics and Automation Engineering Coimbatore India

ISBN: (纸本)9798331529635

This research aims to develop a more accurate and efficient system for classifying skin diseases, including both malignant and non-cancerous conditions. The study utilizes a comprehensive dataset encompassing various skin diseases, including melanomas, basal cell carcinomas, and benign conditions like nevi and warts. To address the challenge of class imbalance with a higher number of melanoma images, the researchers employ techniques like SMOTE (Synthetic Minority Oversampling Technique) to create synthetic data for under-represented classes. The pre-processing stage involves crucial steps like image scaling, augmentation, normalization, and data cleaning to ensure the quality and consistency of the data. Following pre-processing, feature extraction and model training will be conducted. This research has the potential to significantly contribute to the field of dermatology by enabling faster and more precise diagnosis of skin diseases through the use of advanced pre-processing techniques and a diversified dataset. © 2024 IEEE.

关键词： Lung cancer

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Bearing-Only Formation Tracking Control With Time-Varying Leaders' Velocity without Inter-Agent Communication 43

Bearing-Only Formation Tracking Control With Time-Varying Le...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Yuxiao Chen, Tianxing Zhang, Hongwei Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581581

This paper studies the bearing-only formation tracking problem of leader-follower multi-agent systems, where the leaders move at a time-varying velocity. Different from the existing formation tracking methods, the proposed control protocol can be implemented without inter-agent communication. We construct the control protocol via a variable structure approach to track the time-varying velocity and almost achieve the target formation. We also show that the velocity tracking error will converge to zero and the position tracking error can be restricted in an upper bounded set while the upper bound can be arbitrarily minimized by increasing the control gain. Simulation results are presented to illustrate the effectiveness of the control protocol. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Target tracking

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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