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检索条件"任意字段=5th International Conference on Robotics and Automation Systems, Robotics 2010"

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Digital Control Method of Bucket Truck Based on DH Parameter Modeling

Digital Control Method of Bucket Truck Based on DH Parameter...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Xu Jian Beinuo Huang Yan Zhang Jinbin Li Xin Chen Shangchao Xu Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan China School of Automation China University of Geosciences Wuhan China Shan Dong Tai Kai Automobile Manufacturing Co. Ltd Shandong China

In the aspect of electric power maintenance, the control of as bucket truck has always been a difficult problem, causing by its hardware structure. this requires operators to have rich operating experience. However, even so, the operator usually needs to adjust it repeatedly to move the end effector to the appropriate position, which greatly reduces the operation efficiency and increases the risk of the operation. To solve this problem, this paper proposes a digital control method of bucket truck based on DH parameter modeling. In terms of algorithm, we use DH method to establish the mathematical model of the bucket truck and complete the kinematic derivation. In terms of hardware, we modified the bucket truck, adding multiple encoders to obtain the joint values. Experiments show that our theoretical derivation is correct and has high accuracy when applied to the bucket truck.

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Mathematical Modeling of a Mobile Robot's Motion Control System based on LQR

Mathematical Modeling of a Mobile Robot's Motion Control Sys...

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Communications, Information, Electronic and Energy systems (CIEES), international conference on

作者： Aldabergen Bektilevov Lazzat Kurmangalieva Zhanibek Issabekov Makhabbat Zhamuratova Vinera Baiturganova Perizat Rakhmetova School of Transport Engineering and Logistics Named After M. Tynyshpayev Satbayev University Almaty Kazakhstan Dept. of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Abay Myrzakhmetov Kokshetau University Kokshetau Kazakhstan

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Now, the development of mobile robots is a promising industry, as these types of robots are acquiring more and more functionality for solving various tasks and in most cases are developed as universal robots that help perform different tasks simultaneously. In this regard, this article discusses the problem of developing a mathematical model of a motion control system for a helicopter-type mobile robot. A mathematical model of a mobile robot motion control system has been developed, using an unmanned aerial vehicle (UAV) as an example, which allows you to track the flight movement in state space. Simulation model of UAV control system based on a PD-controller has been developed using Matlab/Simulink. the simulation model controls the spatial motion of the UAV through four control channels (roll, pitch, yaw, and altitude). the calculations were performed based on the optimal method using the integral quadratic criterion for a nonstationary linear control system. the simulation results show that the developed mathematical model is an optimal simulation for stabilizing a mobile robot (UAV) in desired positions and orientations.

关键词： Space vehicles Service robots Tracking Aerospace electronics Autonomous aerial vehicles Control systems Mathematical models Mobile robots Motion control Vehicle dynamics

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Neural Network Based Friction Compensation of Motion Control on A 6-DoF Robot Manipulator

Neural Network Based Friction Compensation of Motion Control...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Xiaozhi Zhang Liangyu Chen Engineer Research Center for Intelligent Robotics Ji Hua Laboratory Foshan China School of Information Management Central China Normal University Wuhan China

Friction is the force resisting relative motions between two bodies, it causes cycle oscillations and impairs closedloop tracking performance for robot manipulators. To address this problem, in this paper, we propose a neural network (NN) structure with attention mechanism, which compromises the calculate efficiency and accuracy. In this structure, both instantaneous data and feature abstracted from historical data are utilized as inputs, and the torques needed to compensate for the effects of friction of each joint are the targets. Besides, the proposed NN method are contrasted with XGBoost method and convolutional neural network method to demonstrate that NN can achieve better approximation performance in offline experiment. Training data are collected by traversing through the working velocity of robot joints on the task trajectory, based on which the best model will be produced and is deployed on servo controller of a 6-degree-of-freedom (DoF) robot manipulator. Finally, the NN structure is tested on a real robot manipulator for online validation, the robot manipulator is programmed to run on the same trajectory with random speeds for a period of time. Online experiment results show that the predicted compensating torques applied to robot by adding feedforward control to the current command are always closer to the desired torques, and tracking errors of joints are effectively reduced.

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Hybridizing Long Short-Term Memory Network and Inverse Kinematics for Human Manipulation Prediction in Smart Manufacturing

Hybridizing Long Short-Term Memory Network and Inverse Kinem...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jiarong Yao Chongshan He Kaixu Li Rong Su Keck-Voon Ling School of Electrical and Electronic Engineering Nanyang Technological University Singapore Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Human-Robot Collaboration (HRC) is essential for enhancing productivity and flexibility in smart manufacturing, which poses requirements on accurately predicting the future movements of human operators, especially the trajectories of their upper limbs. However, existing model-based studies on human manipulation prediction lacks consideration of stochasticity and variability while the emerging deep learning-based methods are demanding on data size, which yet makes real-time deployment challenging. therefore, combining the advantages of both model-based and deep learning-based methods, a method for predicting human arm motion, specifically, the position of a worker's wrist in less than 0.5 second, is proposed by hybridizing a Long Short-Term Memory (LSTM) network with an Inverse Kinematics (IK) model. Using historical coordinate sequences of the wrist joint in three-dimensional space in the past multiple frames as input, a neural network is trained to output the predicted coordinates of the wrist joint for the next frame. then IK (Inverse Kinematics) is used to calculate the arm's motion trajectory based on the predicted wrist coordinates. As the predicted wrist coordinates are sequentially used as the input for the next prediction cycle, the prediction is realized over a sliding time window. Evaluation was conducted using both proprietary and open datasets, results demonstrated that our LSTM-IK method achieved high prediction accuracy, with an average distance error of approximately 5 cm, and can adapt to various task scenarios and individual differences. Additionally, comparison with ground truth illustrated the model's ability to handle complex motion patterns, even with partial occlusions or rapid movements.

关键词： Wrist Learning systems Robot kinematics Kinematics Predictive models Manipulators Real-time systems Trajectory Long short term memory Smart manufacturing

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A Multi-Domain Feature Fusion Method for NOx Concentration Prediction in SCR systems

A Multi-Domain Feature Fusion Method for NOx Concentration P...

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IEEE international conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Yuan Yang Zhenhao Tang School of Automation Engineering Northeast Electric Power University Jilin China

Selective Catalytic Reduction (SCR) systems exhibit characteristics such as nonlinearity and significant hysteresis. In order to establish an accurate predictive model for the outlet NOx emission concentration in SCR, a modeling algorithm based on multi-domain feature fusion is proposed. Firstly, the original data is processed using the boxplot method to remove outliers, and the K-nearest neighbors (KNN) algorithm is employed to impute the removed outlier points based on sample distance. the delay time of each variable is estimated using the maximal information coefficient (MI), and the data is reconstructed accordingly. then, deep features are extracted using singular spectrum analysis, and the final input variables for the model are determined using an adaptive composite feature selection algorithm. Finally, a predictive model for the outlet nitrogen oxide concentration in the SCR system is constructed. Experimental results demonstrate that the proposed algorithm achieves an average absolute percentage error (MAPE) of less than 5 % , thus accurately predicting the outlet nitrogen oxide concentration in the SCR system.

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

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Machine Learning and Computer Application (ICMLCA), international conference on

作者： Siwen Chen Xiujuan Zheng Shanghai United Imaging Healthcare Co. Ltd Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness.

关键词： Lithium-ion batteries Time-frequency analysis Accuracy NASA Predictive models Feature extraction Robustness Numerical models Fourier series Convolutional neural networks

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Research on On-line Prediction of Contact Resistance and Maximum Load Capacity of Switchgear Cabinet in Operation

Research on On-line Prediction of Contact Resistance and Max...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Jieshuai Ren Hongbing Li Zifan Dong Song He Jinbin Li Borui Niu Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Research Institute Wuhan China Equipment Division State Grid Hubei Electric Power Co. Ltd. Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

the contact defect of the disconnecting switch contacts in the switchgear cabinet is the direct cause of the overheating defects, which decreases the load capacity of cabinets and may leads to fire and short-circuit accidents. In this paper, an online prediction scheme of contact resistance and load capacity is proposed. the relationship between the steady-state temperature rise of the switchgear contacts and the combination of contact resistance and load current is deduced qualitatively through the thermal circuit model. then the prediction functions which obtain the contact resistance and the maximum load capacity of the switchgear cabinet by measuring the load current and steady-state temperature in contacts are proposed theoretically. A simulation based on temperature-fluid field is conducted to quantitatively verify the proposed prediction functions. the prediction scheme do not require any additional hardware in switchgear cabinets with contact temperature sensors, and can be used to diagnose contact defect before overheating defects occur, which provides important references for the power dispatch of substations and operation & maintenance of switchgear cabinets.

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ARMER: Modular and Semi-Autonomous Supernumerary Robotic Limbs for Disaster Relief 2021

ARMER: Modular and Semi-Autonomous Supernumerary Robotic Lim...

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5th international conference of the robotics Society on Advances in robotics, AIR 2021

作者： Manisekaran, Parthan Shreyanka, S.G. Sujeet Tripathi, Shikha Sudarshan, T.S.B. Centre For Robotics Automation And Intelligent Systems Dept. of ECE PES University Bangalore India

ISBN: (纸本)9781450389716

the design and implementation of a semi-autonomous Supernumerary Robotic Limbs (SRL) for disaster relief to ease the workload of disaster rescue teams during calamities like earthquakes is proposed in this work. the key parameters considered are modularity, ease of use, payload and accuracy. A 3D printed portable Robotic Limb has been designed that can be carried by the user on the shoulder with a backpack-like structure and can be used to push debris along the line of sight. Existing Supernumerary robotic limbs work on the principle of leader-follower concept wherein the user must mimic the action required following which the robot performs the same action. In a time-constrained, unpredictable environment like disaster, this might not be feasible. the proposed design is equipped with an RGBD camera that can detect the debris 'selected' by the user and can autonomously push the debris. this technique can save time and the effort required in leading the robot or remote controlling it. To test the proof of concept, dimensions of the SRL has been downscaled and the accuracy of detection of debris by the robot, maximum payload that can be pushed, and the impact of the limb if equipped by a human user has been computed. We then interpolate and compare the results to the actual scale SRL. the modularity of the SRL has been analyzed and different end-effectors based on the function and capacities required have also been incorporated in the design. © 2021 ACM.

关键词： Robots

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Visual Guidance of Aerial Vehicle for Target Recognition and Pose Estimation

Visual Guidance of Aerial Vehicle for Target Recognition and...

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IEEE international conference on Cyber Technology in automation, Control, and Intelligent systems

作者： Zhaoyang Zhang Guangyu Zhang Liying Yang Yuqing He Siliang Li Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang China

this paper proposes a deep learning-based target pose estimation method for aerial vehicle, a robotic system combining a rotary-wing UAV and a robotic arm, which is difficult to accurately acquire the pose of the target when performing tasks in the field environment. Firstly, in the offline stage, the YOLOv5 model is improved by adding angle prediction branches to make it capable of pose estimation; secondly, the offline model is used to complete target recognition, target segmentation and pose estimation in combination with online images, and the online visual point cloud is combined with statistical filtering to complete the position estimation of targets; Finally, the effectiveness and feasibility of this visual guidance method is verified through indoor static experiments and outdoor flight experiments, and this method successfully guided the aerial vehicle to complete the torch docking task.

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Optimization of Image Preprocessing and Background Influences using a Depth Camera for Person Re-Identification on a Mobile Robot

Optimization of Image Preprocessing and Background Influence...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Sebastian Flores Zeynep Boztoprak Jana Jost Department of AI and Autonomous Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Department of Robotics and Cognitive Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany

In this paper, we optimize datasets with different image preprocessing techniques for person re-identification using the ResNet18 model on a mobile robot with limited hardware, e.g., computational power and depth camera. For this, we create 16 datasets for which we discovered that the inverted original images, from the IR gray value images of the depth camera, results in the highest values with an r1, r5 and mAP of 98.48 %,99.82 % and 80.86 %. Additionally, we explore the cross-dataset evaluation for the 16 datasets to examine the robustness of our model, which points to a low generalizability. the scores are associated with the similarity between the trained and evaluated dataset.

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