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6th International Conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Li, Lili Li, Wenwei Huang, Jinglun School of Automation Science and Engineering South China University of Technology Guangzhou China Institute of Intelligent Manufacturing Shunde Polytechnic Foshan China Department of Technical Research South China Robotics Innovation Research Institute Foshan China Foshan Zhiyouren Technology Co. Ltd. Foshan China School of Electronics and Information Engineering Wuyi University Jiangmen China

ISBN: (纸本)9798331541699

In order to quickly plan the shortest path for mobile robot in two-dimensional space, a two-stage obstacles avoidance path planning algorithm based on cellular automata was presented in this paper. For the first stage, the distance between each free cell to the ending cell was gradually generated by the evolution of cellular automata, and the algorithm’s cellular space, cellular states, Moore type neighbors and evolutionary rules were described, respectively. For the second stage, by using the idea of the negative gradient, a method of generating the shortest path based on the cells’ state values was proposed. Accordingly, the corresponding flow chart and Matlab programme of the algorithm were designed. The effectiveness of the algorithm was verified by the simulation and the physical experiment. The results showed that the shortest path in complex environment can be efficiently planned by using our cellular automata algorithm. © 2024 IEEE.

关键词： Microrobots

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Fault Diagnosis of Quadruped Robot Joint Modules Based on TDIC and Random Forest

Fault Diagnosis of Quadruped Robot Joint Modules Based on TD...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yufan Chen Beibei Fan Xin Xiong Mapping Shi School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

As robotics technology advances, quadruped robots are increasingly instrumental in diverse applications, including rescue operations and healthcare. However, the intricate operating conditions expose the components within the joint modules of these robots to alternating and impact loads. Consequently, research into signal processing and fault diagnosis techniques for planetary gearboxes in joint modules holds significant engineering importance. Given the challenges associated with fault signal extraction from planetary gearboxes, this study proposes a novel fault feature extraction method based on Time-Dependent Intrinsic Correlation (TDIC), integrated with a random forest classifier for fault classification. This approach leverages signal frequency analysis to adaptively extract segments characterized by high correlation among different physical field signals as fault features. The random forest model is utilized for fault classification and its performance is compared against CNN, KNN, and XGBoost models. Experimental results validate the superior fault classification efficacy of the proposed method.

关键词： Fault diagnosis Adaptation models Correlation Accuracy Computational modeling Feature extraction Real-time systems Quadrupedal robots Random forests Load modeling

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Improvement of Unconstrained Appearance-Based Gaze Tracking with LSTM

Improvement of Unconstrained Appearance-Based Gaze Tracking ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Guoxu Dai, Lihong Gao, Qing Gao, Hongwei Ju, Zhaojie Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institute of Robotics and Intelligent Manufacturing School of Science and Engineering Chinese University of Hong Kong Shenzhen China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Gaze tracking is not only an important research direction in computer vision but also an important non-verbal clue in human life. What is important is that the direction of gaze can be used as a reference for judging a person's intentions. In order to improve the accuracy of predicting gaze direction, a model of 3D gaze tracking based on bidirectional Long Short-Term Memory (LSTM) is proposed in this paper. The backbone network of the model is ResNet and its variants. The output of the model is the angular error of gaze direction. To improve the accuracy of the model prediction, the attention mechanism is adopted in this work. The ablation experiments are conducted on the selected Gaze360, which is a dataset with sufficiently large and diverse data. The angular error of the proposed model decreases from 13.5° to 12.6°. © 2022 IEEE.

关键词： Eye tracking

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A Real-Time AGV Gesture Control Method Based on Body Part Detection 1

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

作者： Xu, Yuhang Gao, Qing Yu, Xiang Zhang, Xin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang110016 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

The intelligent control of Automated Guided Vehicles (AGV) has essential research significance and application in logistics loading, unmanned driving, and emergency rescue. As an idealized human-computer interaction method, gesture has tremendous expressive power. Therefore, the gesture-based AGV control method is the mainstream. However, In a complex environment, noise interference can affect the precise and real-time control of AGV. To deal with this problem, a real-time AGV gesture control method based on human body part detection is proposed. We design a simple AGV control method based on human gestures by the relative relationship between human body parts in space. We extend a new branch on the Fully Convolutional One-Stage Object Detection (FCOS), which constrains the detection range of human parts. This method subtly associates the human parts with the human body, which vastly improves the anti-interference capability of gesture recognition. We train the network end-to-end on the COCO Human Parts dataset and achieve a detection accuracy of 35.4% of human parts. In addition, We collect a small dataset for validating the gesture recognition method designed in this paper and achieves an accuracy of 96.1% with a detection speed of 17.23 FPS. Our method achieves precise and convenient control of AGVs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Object detection

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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

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

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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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.

关键词： Formation tracking Bearing-only measurement Multi-agent system

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Research on Active Disturbance Rejection Control of PMSM Based on Improved BOA

Research on Active Disturbance Rejection Control of PMSM Bas...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Tong Chen Qiang Jiang Bopi Jin ShengXuan Yang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to solve the problems of low control accuracy, poor anti-interference ability and poor robustness due to the interference and parameter changes of the servo system of permanent magnet synchronous motor (PMSM) under PI control, an improved active disturbance rejection control strategy is proposed. On the basis of linearisation and bandwidth of the extended state observer, the controller parameters are combined with the bandwidth in the project. Furthermore, to improve the parameter optimization tuning effect, the butterfly optimization algorithm is not high in convergence accuracy, speed and stationarity, and it is easy to avoid falling into the problem of local optimum, the reverse learning strategy, Cauchy variation and sine and cosine guidance mechanism are introduced to optimise the algorithm, and simulation experiments are carried out. The results show that compared with the traditional Proportion Integral(PI) control and active disturbance rejection control, the proposed method can quickly track a given speed, has higher control speed, and can be used to control the local optimum. The results show that compared with the traditional PI control and active disturbance rejection control, the proposed method can quickly track a given speed, has higher control accuracy and anti-interference ability, and effectively ensures the robustness of the PMSM servo system.

关键词： Robust control Accuracy Simulation Heuristic algorithms Signal processing algorithms Bandwidth Interference Robustness Servomotors Optimization

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Research of Brain Controlled Lower Limb Rehabilitation Exoskeleton

Research of Brain Controlled Lower Limb Rehabilitation Exosk...

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Hualong Sheng Xiaoyan Deng Yinke Wen Zhuliang Yu School of Automation Science and Engineering South China University of Technology Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Recently, lower limb exoskeleton robots have been widely used to assist stroke patients in completing lower limb rehabilitation training, and active rehabilitation training guided by patients' motor intentions can stimulate rehabilitation enthusiasm and improve training effectiveness. In order to achieve motion intention recognition and control lower limb exoskeletons to complete rehabilitation training, in this paper, a lower limb exoskeleton control method based on a brain computer interface based on motion imagery signals and lower limb joint angle gait data planning is proposed. Firstly, the common spatial pattern method is used to extract electroencephalogram (EEG) features, and convolutional neural network is employed to recognize motor intentions. Then, based on Fourier series fitting, the lower limb joint angle data is optimized to complete gait planning, and the lower limb exoskeleton is controlled to perform three training actions: walking straight, going up and down stairs. Finally, experiments on EEG recognition and exoskeleton control were completed. The results showed that the proposed method can recognize EEG signals and control the lower limb exoskeleton to drive the subject to complete corresponding rehabilitation training actions.

关键词： Training Legged locomotion Limbs Exoskeletons Stairs Stroke (medical condition) Motors Electroencephalography Planning Robots

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A Lightweight Network Framework for Fault Diagnosis of Quadruped Robot Joint Modules

A Lightweight Network Framework for Fault Diagnosis of Quadr...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Maoping Shi Xin Xiong Beibei Fan Yufan Chen School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

With the widespread deployment of quadruped robots across various application scenarios, they are required to adapt to diverse motion patterns when performing different tasks. This necessity imposes complex stresses and loads on the components within the robot's joint modules during movement, leading to a higher risk of wear and failure. To address these challenges, this study proposes a novel lightweight multi-scale neural network based on a dual attention mechanism and Depthwise Separable Convolution (DSconv), named Atten-MSDsN, for diagnosing faults in joint modules. This approach captures critical fine-grained features across the global scope of the signal through the attention mechanism, extracts multi-scale features from fault signals using convolution kernels of varying sizes, and reduces the number of parameters in the feature extractor via DSconv. Experimental analysis demonstrates that the Atten-MSDsN framework offers the advantages of being lightweight and robust.

关键词： Fault diagnosis Attention mechanisms Convolution Scalability Transfer learning Predictive models Feature extraction Quadrupedal robots Kernel Stress

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Human-machine shared control method for brain-controlled wheeled robot

Human-machine shared control method for brain-controlled whe...

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Duo Han Xiaoyan Deng Zhuliang Yu Zhenghui Gu School of Automation Science and Engineering South China University of Technology Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Human-machine shared control has been verified to be an effective method for brain-controlled robot. This paper proposes a shared control system for wheeled robots, which converts the EEG signals generated by SSVEP into the BCI command of the wheeled robot through vector fusion, and designs the RAC method to adjust the human-machine control weight based on the confidence evaluation of the EEG signals and the robot environment evaluation. Considering the problem of human-machine conflict, this paper adopts a "people-oriented" strategy and proposes the SCR-TEB algorithm to resolve human-machine conflicts. Online experiments have proved that the method proposed in this paper can effectively improve the success rate of the task and give people a stronger sense of control.

关键词： Human-machine systems Heuristic algorithms Redundancy Robot sensing systems Electroencephalography Vectors User experience Trajectory Mobile robots Signal resolution

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