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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ethan Canzini Simon Pope Ashutosh Tiwari Department of Automatic Control & Systems Engineering University of Sheffield UK Airbus Robotics Airbus UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Many robotic manipulation tasks are constrained due to kinematic limitations placed on the object being manipulated. This increases the complexity of manipulation tasks that operate in high dimensions, leading to increased risk that sampling based planners are unable to find optimal solutions. Whilst trajectory optimisation methods provide guaranteed optimal solutions when implementing constraints, they only provide locally optimal solutions in sequential decision-making and struggle to provide globally optimal paths. These constraints can be incorporated into the probabilistic latent spaces by using demonstrations that satisfy the constraint function. However whenever constraints change or a manipulator must perform different tasks the network must be retrained to accommodate the new constraints. In this paper, we provide an approach that allows the training of a single learned manifold that can be augmented to determine the constraint manifold for the manipulation task. Using this manifold, the geodesic between two points can be computed using policy search to solve the cost function associated with the geodesic curve length ${\mathcal{L}_\gamma }$. We provide comparisons in terms of path length against popular path planning algorithms with different kinematic constraints, demonstrating our method’s ability to find optimal shortest paths on constraint manifolds.

关键词： Manifolds Training Runtime Optimization methods Kinematics Reinforcement learning Probabilistic logic Real-time systems Trajectory Splines (mathematics)

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Solvability of the Inverse Optimal control problem based on the minimum principle

arXiv

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arXiv 2024年

作者： Islam, Afreen Herrmann, Guido Carrasco, Joaquin The Control Systems and Robotics Group Department of Electrical and Electronic Engineering The University of Manchester United Kingdom

In this paper, the solvability of the Inverse Optimal control (IOC) problem based on two existing minimum principal methods, is analysed. The aim of this work is to answer the question regarding what kinds of trajectories, that is depending on the initial conditions of the closed-loop system and system dynamics, of the original optimal control problem, will result in the recovery of the true weights of the reward function for both the soft and the hard-constrained methods [1], [2]. Analytical conditions are provided which allow to verify if a trajectory is sufficiently conditioned, that is, holds sufficient information to recover the true weights of an optimal control problem. It was found that the open-loop system of the original optimal problem has a stronger influence on the solvability of the Inverse Optimal control problem for the hard-constrained method as compared to the soft-constrained method. These analytical results were validated via simulation. © 2024, CC BY.

关键词： Closed loop systems

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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Adaptive Direct Compensation of External Disturbances for Mimo Linear systems

Adaptive Direct Compensation of External Disturbances for Mi...

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International Russian Automation Conference (RusAutoCon)

作者： Bui Van Huan Alexey A. Margun Artem S. Kremlev Department of Control Systems and Robotics ITMO University St. Petersburg Russia Department of Control Systems and Robotics IPME RAS ITMO University St. Petersburg Russia

The paper proposes a new algorithm for compensating external disturbances for a class of multi-input multi-output (MIMO) linear systems. The solution of the problem is based on the use of the internal model principle and the extended error adaptation algorithm. It is assumed that the disturbance is the output of an autonomous linear generator with unknown parameters. At the first stage, a full-order unknown input observer (UIO) is synthesized to solve the problem of estimating the state vector of the plant. Then a new observer of external disturbance is formed on the basis of state vector estimates. At the last stage, based on the new observer's estimates, a system with an extended state vector is formed for that a controller providing compensation of disturbance is constructed. The performance of the obtained results is confirmed using computer simulation in MATLAB Simulink.

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Development of a Model for Texture Analysis of Machined Parts on CNC Machines

Development of a Model for Texture Analysis of Machined Part...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ilya Kovalev David Malykh Alexander Kiselev department of Computer Control Systems MSUT “STANKIN” Moscow Russia department of Robotics and Mechatronics MSUT “STANKIN” Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This work is devoted to the development of a software module for analyzing the textures of machined parts on CNC machines using computer vision methods. The main goal of the research is to create a tool capable of analyzing geometric parameters of products based on texture properties of materials, which is especially relevant in the conditions of high-volume production. The paper considers modern methods of texture analysis, as well as their robustness to various disturbances such as illumination changes, noise, blurring, rotation and image scaling. An analysis system that combines binocular and texture methods of image processing is proposed, which makes it possible to improve the accuracy of measuring distances to objects on the basis of binocular algorithm, by means of additional analysis of texture properties of surfaces of the processed part.

关键词： Computer vision Image analysis Software algorithms Noise User interfaces Software Robustness Classification algorithms Surface texture Surface treatment

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Modern Approaches for Equipment's Technical State Prediction

Modern Approaches for Equipment's Technical State Prediction

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Information, control, and Communication Technologies (ICCT), International Conference on

作者： Stepan Bratchikov Department of Control Systems and Robotics ITMO University Saint-Petersburg Russia

Any manufacturing process aims to become more efficient. There is a plenty ways to achieve this. One of those is industrial equipment technical state prediction. Some of the methods are considered. Also, inclusion of modern machine learning and digital twins techniques is reviewed. As a result, the method of describing a production process as set of separate operations characterized by the parameters of the equipment involved in order to predict the output quality of produced items is introduced.

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Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras 26

Memory-Efficient Graph Convolutional Networks for Object Cla...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Jeziorek, Kamil Pinna, Andrea Kryjak, Tomasz AGH University of Krakow Embedded Vision Systems Group Department of Automatic Control and Robotics Krakow Poland Sorbonne Universite CNRS LIP6 ParisF-75005 France

ISBN: (纸本)9798350304985

Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high dynamic range, low latency, and resistance to image blur. One promising approach for analyzing event data is through graph convolutional networks (GCNs). However, current research in this domain primarily focuses on optimizing computational costs, neglecting the associated memory costs. In this paper, we consider both factors together in order to achieve satisfying results and relatively low model complexity. For this purpose, we performed a comparative analysis of different graph convolution operations, considering factors such as execution time, the number of trainable model parameters, data format requirements, and training outcomes. Our results show a 450-fold reduction in the number of parameters for the feature extraction module and a 4.5-fold reduction in the size of the data representation while maintaining a classification accuracy of 52.3%, which is 6.3% higher compared to the operation used in state-of-the-art approaches. To further evaluate performance, we implemented the object detection architecture and evaluated its performance on the N-Caltech101 dataset. The results showed an accuracy of 53.7% mAP@0.5 and reached an execution rate of 82 graphs per second. © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Cameras

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Cascade LTI System for Acoustic Emission Digital Signal Processing: Result Visualization via Scalogram for Defect Pattern Representation

Cascade LTI System for Acoustic Emission Digital Signal Proc...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Modern acoustic emission (AE) diagnostic systems are sensitive instruments for detecting developing defects. A significant limitation of the AE method is the difficulty of isolating signals against the background of interference, which significantly reduces the signal-to-interference ratio. Therefore, in this article a cascade filtering scheme for visualizing acoustic signatures through a scalogram is proposed to isolate AE signals that characterize the process of defect formation. Acoustic portraits of the isolated signals characterizing the process of defect formation are given. It is shown that the amplitude of the AE signal from a defective structure during crack formation is characterized by the highest values of the signal amplitude and has a rich frequency-time structure, the signal shape of which is determined by discrete AE.

关键词： Linear systems Visualization Time-frequency analysis Filtering Shape Instruments Interference Digital signal processing Acoustic emission Signal to noise ratio

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Image Segmentation System of Soft Capsules Based on Deep Learning

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Journal of Institute of control, robotics and systems 2024年第6期30卷 607-613页

作者： Jung, Young-Gil Park, Tae-Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Pharmacists in conventional pharmacies and hospitals prepare and visually inspect medications before dispensing these medications to patients, thereby posing risks of exposure through skin contact or inhalation, as well as the potential for generating compounding errors such as incorrect medication types or quantities. To address the limitations of the manual compounding processes, automated compounding and medication-verification systems have been introduced and implemented. The automated compounding system prepares medications in pouch units based on prescriptions written by doctors, whereas the medication verification system verifies whether the types and quantities of the medication dispensed in these pouches match the prescription information. Medications are categorized broadly into solid tablets, hard capsules, and soft capsules. Color and shape information is used to classify medication types;solid tablets and hard capsules can be classified using rule-based image processing techniques such as image binarization. However, extracting shape information from soft capsules through which light passes is impossible via image binarization alone;therefore pattern matching is employed. Nevertheless, soft capsules change their forms, causing significant variations in pattern-matching similarity;therefore the detection accuracies of these capsules are low. To address this issue, this paper proposes a deep learning-based method for detecting soft capsules. © ICROS 2024.

关键词： Pattern matching

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