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A Control Algorithm for an Aerial Co-Manipulation System

A Control Algorithm for an Aerial Co-Manipulation System

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Fan Chen Xinyue Zhang Yuxin Li Yixin Wang Electronic Information Engineering Southwest University of Science and Technology Mianyang China Communication Engineering Southwest University of Science and Technology Mianyang China Robotics Engineering Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

During aerial co-manipulation, addressing the disturbances caused by the motion of the lower UAV on the aerial dexterous manipulation platform and the perturbations induced by the manipulation platform's own movement, as well as the issue of the inadequate number of existing simulation platforms, this study introduces an adaptive sliding mode control approach that leverages radial basis function neural networks and establishes a simulation platform for aerial comanipulation within the Gazebo environment. In the control method, the Lagrangian equation is employed for standardized modeling, and the model is simplified through projection. the universal approximation property of neural networks is utilized for adaptive rate optimization. Additionally, a sliding mode algorithm grounded on the exponential reaching law is implemented to bolster robustness. To further mitigate chattering, the hyperbolic tangent function is employed. Finally, the results were verified through trajectory tracking in Matlab/Simulink, ensuring that the system converged in about 1 second and achieved micron-level multi-channel tracking error accuracy, effectively reducing chatter. the Gazebo simulation platform sets up three aerial dexterous manipulation platforms capable of performing basic aerial tasks, and control stability is verified using PID algorithms.

关键词： Adaptation models Accuracy Uncertainty Trajectory tracking Heuristic algorithms Visual systems Mathematical models Stability analysis Sliding mode control Lyapunov methods

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Modeling the Variable Pitch Screws by Using the Envelope theory Based on Cubic H-Bézier

Modeling the Variable Pitch Screws by Using the Envelope The...

引用

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

作者： Yanwei Wu Gengbin Zhang Yu Ling Changqing Song Tie Zhang Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

this paper presents a novel approach to derive analytical expressions for variable pitch screw threads by employing three-segment composite H-Bézier curves as transitional curves at the beginning and end of the screw thread. Additionally, it introduces a methodology to represent the material surface as enveloping single-parameter surface families, enabling the calculation of the helical surface of variable pitch screw threads. the geometric model developed through this method finds applications in post-processing tasks such as material property analysis, numerical control machining, finite element analysis, and more. Finally, the proposed design and calculation methods for variable pitch screw threads are modeled and simulated within the MATLAB environment, demonstrating their practical value and feasibility.

关键词： Shape Design methodology Geometric modeling Machining Fasteners Harmonic analysis Polynomials Numerical models Trajectory Optimization

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Barrier Lypanunov Function-based Adaptive Control of an Ankle Exoskeleton

Barrier Lypanunov Function-based Adaptive Control of an Ankl...

引用

automation, Control and robotics Engineering (CACRE), international conference on

作者： Yapeng Wang Yunhai Geng Research Center of Satellite Technology Harbin Institute of Technology Harbin China Beijing Xinfeng Aerospace Equipment Co. Ltd. Beijing China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the application of exoskeleton robots in industrial assembly and other fields plays a significant role in alleviating low back pain caused by work-related musculoskeletal disorders and improving work efficiency. Currently, the ankle joints of lower limb exoskeleton robots used in this field are all treated as passive joints. Ankle is important for weight-bearing joints of human lower limbs in the stability and flexibility of daily activities. To this end, the authors of this paper have designed a cable-driven ankle exoskeleton structure to achieve active dorsiflexion and plantarflexion movements in the sagittal plane. To compensate the modeling error and estimate the perturbation, neural network (NN) and nonlinear disturbance observer (NDO) are used. A time-varying barrier Lyapunov function is used to design an adaptive backtracking controller to constrain the system output error and ensure the tracking performance and safety of the robot. A healthy subject participates in the experiment wearing the cable-driven ankle exoskeleton. the experimental is conducted to validate performance of the proposed controller.

关键词： Ankle Backtracking Adaptation models Service robots Perturbation methods Exoskeletons Artificial neural networks

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Anesthesia Control Using Fractional Order Controller

Anesthesia Control Using Fractional Order Controller

引用

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

作者： Eva-H. Dulf Paul A. Pintea Cristina I. Muresan Department of Automation Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Anesthesia plays a crucial role in every surgery. Clinicians are faced with numerous conditions that need to be monitored and controlled. A computerized monitoring and control system would help them in this challenging task. Designing a control law for general anesthesia presents numerous challenges, particularly due to the nonlinear mapping of outputs and the complexity of achieving a feasible input trajectory. While the internal states of the mathematical model are linear, the output exhibits nonlinear behavior. this paper introduces a decomposition of the standard pharmacokinetic-pharmacodynamic (PK-PD) model to develop an effective control strategy for drug administration in anesthesia. It is proposed a method to control the bispectral index (BIS), the main measure of unconsciousness of the patient. this approach aims to optimize anesthesia delivery by maintaining the BIS within a desired range, thereby enhancing patient safety and comfort. the methodology involves designing a fractional order controller to accurately track BIS reference values and adjust anesthetic infusion rates. the effectiveness of the controller is validated through simulations, demonstrating its potential to enhance the precision of anesthesia management. Key findings reveal that such type of control outperforms traditional methods in maintaining target BIS levels, minimizing the risks of over- or under-dosing.

关键词： Drugs Robust control Target tracking Surgery Anesthesia Mathematical models Trajectory Safety Standards Robots

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Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis

Sparse Causal Residual Neural Network for Linear and Nonline...

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

作者： Jiawei Chen Chunhui Zhao Youxian Sun School of Mechanical Engineering Zhejiang University Hangzhou China the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. the advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. the feasibility of the proposed method is illustrated through the benchmark TE process.

关键词： Time series analysis Predictive models Optimization Reactive power Fitting Fault diagnosis Residual neural networks

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Multi-Scale-Rhythm Attention Feature Contrastive Learning for Epileptic Seizure Prediction

Multi-Scale-Rhythm Attention Feature Contrastive Learning fo...

引用

Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Yifan Wang Wenkang Liu Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

the electroencephalogram (EEG) signals based epileptic seizure prediction remains a pivotal technique to improve the living standards for epilepsy patients. However, recent prediction models focus on the local feature extraction with smaller receptive field, which ignores multi-level global feature changes in pre-ictal period. Besides, traditional supervised or semi-supervised learning strategies requires sufficient data in individual patient, and it is difficult to handle the representation learning of imbalanced samples of intra-patient EEG data, yielding a suboptimal performance in predicting seizures. To overcome above limitations, a multi-scale-rhythm attention feature contrastive learning (MSR-AFCL) model is proposed for the prediction of seizures in individual patients. First, a feature extractor called the multi-scale-rhythm (MSR) employs multi-scale dilated convolution module and multi-rhythm power spectrum density (PSD) estimate module, so as to extract multi-level spatio-temporal features and rhythmic temporal-spectral features with large receptive field. then, an attention feature contrastive learning (AFCL) strategy is developed, and it has the capability to dynamically adjust the intra-class and inter-class distances of attention fusion representations. the experimental results indicate that our proposed MSR-AFCL model achieves outstanding seizure warning when evaluated on the publicly available CHB-MIT dataset, and it outperforms the state-of-the-art methods.

关键词： Representation learning Service robots Epilepsy Contrastive learning Predictive models Semisupervised learning Feature extraction Brain modeling Electroencephalography Standards

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Batch-Normalization-based Soft Filter Pruning for Deep Convolutional Neural Networks

Batch-Normalization-based Soft Filter Pruning for Deep Convo...

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

作者： Xiaozhou Xu Qiming Chen Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Zhejiang University Hangzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

As convolutional neural network contains many redundant parameters, a lot of methods have been developed to compress the network for accelerating inference. Among these, network pruning, which is a kind of widely used approaches, can effectively decrease the memory capacity and reduce the computation cost. Herein, we propose a competitive pruning approach based on Soft Filter Pruning (SFP) by taking account of the scaling factors y of Batch Normalization (BN) layers as the criterion of filter selection strategy. During the soft pruning procedure, in each epoch only y values of BN layers less than threshold are set to zero instead of setting the weights of selected filters in convolutional layers to zero. Compared to the existing approaches, the proposed method can obtain a highly increased accuracy on image recognition. Notably, on CIFAR-10, the proposed method reduces the same 40.8% FLOPs as SFP on ResNet-110 with even 0.87% top-1 accuracy improvement.

关键词： Training Convolution Convolutional neural networks Computational modeling Image recognition Acceleration Adaptation models

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Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles

Instance Segmentation with Unsupervised Adaptation to Differ...

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

作者： Manuel Diaz-Zapata Özgür Erkent Christian Laugier Univ. Grenoble Alpes INRIA Chroma Team France INRIA Rhône-Alpes France

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Detection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which can be adapted to new conditions without requiring the labels from the new condition. Furthermore, the performance of the instance segmentation does not degrade in detection of the objects in the original condition after it adapts to the new condition. To our knowledge, currently there are not other methods which perform unsupervised domain adaptation for the task of instance segmentation using non-synthetic datasets. We evaluate the adaptation capability of our method on two datasets. Firstly, we test its capacity of adapting to a new domain; secondly, we test its ability to adapt to new weather conditions. the results show that it can adapt to new conditions with an improved accuracy while preserving the accuracy of the original condition.

关键词： Image segmentation Semantics Adaptation models Meteorology Autonomous vehicles Gallium nitride Training

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Open graphics system for 3D-mapping validation on robotics

Open graphics system for 3D-mapping validation on robotics

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IEEE international conference on Electronics, Circuits and Systems (ICECS)

作者： J.V. Catret M. Mellado D. Puig Departamento de Ingenieria de Sistemas y Automatica (DISA) Universidad Politecnica de Valencia Valencia Spain

An innovative product for 3D-mapping validation on robotics is introduced on this paper as result of the application of a multi-robot programming and simulation software to that purpose. the software, called Virtual Robot Simulator (VRS), has an open design that facilitates the integration of user applications by means of external components that give access to its main functionalities. For example, realistic and smart graphics representation of 3D-maps can be easily generated using simple functions. In fact, the user can concentrate the efforts on implementing sensor models, signal filters, 3D-mapping methods and so on, and leave the arduous work of graphics representation to VRS. then VRS acts as a "true" virtual cell composed of robots with sensors moving on their environment and reflecting the actions of the user's application. Even more, the specific physical sensor and robot is transparent to the user when decides to take the best of the hierarchical software structure offered to manage this kind of system. A practical example is commented on in the paper.

关键词： Graphics Application software Robot programming Robot sensing systems robotics and automation Mobile robots Robot control Remote monitoring Control system synthesis Automatic programming

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A Kalman Filter-Based Submersible Position Prediction Model and a Multi-Target Dynamic Search and Rescue Scheme

A Kalman Filter-Based Submersible Position Prediction Model ...

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

作者： Yujie Tian Yuxi Luo Sitong Zhou Kun Zhang Yan Wang School of Astronautics Beihang University Beijing China School of Mechanical Electrical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Aiming at the deep-sea submarine search and rescue problem, this paper proposes a universally applicable submarine position prediction model and search model. the position prediction of the submersible is realized by Kalman filtering and dynamics modeling. Meanwhile, Monte Carlo method is used to construct the initialized population of the motion trajectory and optimize the search path step by step by iterative genetic algorithm. In this paper, the optimal search path is taken as the objective, the main ship SAR trajectory is simulated, and the probability of the diver is fitted as a function of time and cumulative search results. For the multiple submersible search and rescue problem, this paper proposes two possible search and rescue schemes and compares the search and rescue time. then we propose the optimal multi-objective dynamic search and rescue method. In addition, this paper adopts the G1-entropy weight-coefficient of variation combined assignment method to evaluate different search devices and creatively adopts the CRITIC method to secondary assign the weights obtained from different evaluation methods to select the optimal search device. Finally, sensitivity and robustness analysis are also carried out in this paper.

关键词： Analytical models Sensitivity Predictive models Search problems Robustness Trajectory Kalman filters Vehicle dynamics Underwater vehicles Genetic algorithms

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