检索结果-内蒙古大学图书馆

Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery

学校读者我要写书评

暂无评论

Constrained Visual-Inertial Localization With Application An...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Regine Hartwig Daniel Ostler Jean-Claude Rosenthal Hubertus Feuß ner Dirk Wilhelm Dirk Wollherr Surgical Department Research Group MITI Technical University of Munich (TUM) Germany Department of Electrical and Computer Engineering Chair of Automatic Control Engineering Technical University of Munich (TUM) Germany Fraunhofer HHI Berlin Germany

We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements. We use residuals from the different modalities to jointly optimize a global cost function. The residuals emerge from IMU measurements, stereoscopic feature points, and constraints on possible solutions in SE(3). In settings where dynamic disturbances are frequent, the residuals reduce the complexity of the problem and make localization feasible. We verify the advantages of our method in a suitable medical use case and produce a dataset capturing a minimally invasive surgery in the abdomen. Our novel clinical dataset MITI is comparable to state-of-the-art evaluation datasets, contains calibration and synchronization and is available at [1].

关键词：

Multi-loop Sliding-Mode control for a Battery Charger Using a Quadratic Buck Converter

学校读者我要写书评

暂无评论

Multi-loop Sliding-Mode Control for a Battery Charger Using ...

IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Oswaldo López-Santos F. Flores-Bahamonde C. A. Torres-Pinzón Reham Haroun Hugo Valderrama-Blavi Juan A. Garriga Luis Martínez-Salamero Group of Automatic Control and Industrial Electronics Rovira i Virgili University Tarragona Spain Energy Transformation Center Faculty of Enigineering Universidad Andres Bello Chile Faculty of Electronic Engineering Universidad Santo Tomás Colombia Department of Computer Science and Industrial Engineering University of Lleida Lleida Spain

In this paper, the quadratic buck converter (QBC) is proposed as competitive alternative to implement a battery charger. Since, QBC is a high order system, the required control is designed to follow the conventional constant-current constant-voltage regime by means of three loops. Namely, i) an inner-loop operating in sliding mode to control the current of the closest inductor to the input port providing the properly stability of the system, ii) a first outer loop designed to regulate the battery voltage providing the reference of the inner loop, and finally iii) a second outer loop to regulate the battery current modifying the reference of the voltage loop. Proportional Integral (PI) controllers are used in both outer loops. Simulation results are presented validating the theoretical study.

关键词：

A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

学校读者我要写书评

暂无评论

Real-Time Object Detection and Robotic Manipulation for Agri...

IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

Reliability of Tumour Classification from Multi-Dimensional DCE-MRI Variables using Data Transformations

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Notley, S.V. Thacker, N.A. Horsley, L. Little, R.A. Watson, Y. Mullamitha, S. Jayson, G.C. Jackson, A. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Division of Informatics Imaging and Data Science University of Manchester Manchester United Kingdom Department of Medical Oncology The Christie NHS Foundation Trust Wilmslow Road ManchesterM20 4BX United Kingdom

Summary mean DCE-MRI variables show a clear dependency between signal and noise variance, which can be shown to reduce the effectiveness of difference assessments. Appropriate transformation of these variables supports statistically efficient and robust comparisons. The capabilities of DCE-MRI based descriptions of hepatic colorectal tumour classification was assessed, with regard to their potential for use as imaging biomarkers. Four DCE-MRI parameters were extracted from 102 selected tumour regions. A multi-dimensional statistical distance metric was assessed for the challenging task of comparing intra- and inter- subject tumour differences. Statistical errors were estimated using bootstrap resampling. The potential for tumour classification was assessed via Monte Carlo simulation. Transformation of the variables and fusion into a single chi-squared statistic shows that inter subject variation in hepatic tumours is measurable and significantly greater than intra-subject variation at the group level. However, reliability analysis shows that, at current noise levels, individual tumour assessment is not possible. Appropriate data transforms for DCE-MRI derived parameters produce an improvement in statistical sensitivity compared to conventional approaches. Reliability analysis shows, that even with data transformation, DCI-MRI variables do not currently facilitate good tumour discrimination and a doubling of SNR is needed to support non-trivial levels of *** Codes 92-08 © 2023, CC BY.

关键词： Tumors

Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

学校读者我要写书评

暂无评论

Continual Learning for Robust Gate Detection under Dynamic L...

International Joint Conference on Neural Networks (IJCNN)

作者： Zhongzheng Qiao Xuan Huy Pham Savitha Ramasamy Xudong Jiang Erdal Kayacan Andriy Sarabakha ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Institute of Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

关键词： Continuing education Robot kinematics Neural networks Lighting Logic gates Robustness Real-time systems

Distributed Time-Varying Gaussian Regression via Kalman Filtering

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Taddei, Nicola Maggioni, Riccardo Eising, Jaap De Pasquale, Giulia Dörfler, Florian Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612 AP Netherlands

We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control applications, whenever real-time and robust estimation of dynamic cost/reward functions in safety critical settings has to be performed. In this paper, we adopt a finite-dimensional approximation of a Gaussian Process, corresponding to a Bayesian linear regression in an appropriate feature space, and propose a new algorithm, DistKP, to track the time-varying coefficients via a distributed Kalman filter. The proposed method works for arbitrary kernels and under weaker assumptions on the time-evolution of the function to learn compared to the literature. We validate our results using a simulation example in which a fleet of Unmanned Aerial Vehicles (UAVs) learns a dynamically changing wind field. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Rikos, Apostolos I. Oliva, Gabriele Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma via Álvaro del Portillo 21 Rome00128 Italy The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

In this paper, we present a distributed algorithm which implements the k-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm. © 2022, CC BY.

关键词： K-means clustering

Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai200433 China Iowa State University AmesIA50011 United States

The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Crops