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NeRF in robotics: A Survey

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

Optimal control of Automatic Manipulator for Elimination of Galvanic Line Load Oscillation

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Periodica polytechnica Electrical engineering and computer s...

Periodica polytechnica Electrical engineering and computer science 2021年第2期65卷 91-105页

作者： Autsou, Siarhei Rassõlkin, Anton Vaimann, Toomas Demidova, Galina Saroka, Viktor Karpovich, Dzmitry Kallaste, Ants Department of Automation of Production Processes and Electrical Engineering Faculty of Chemical Technology and Technique Belarussian State Technological University 13a Sverdlova str. Minsk220006 Belarus Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology 5 Ehitajate street Tallinn19086 Estonia Faculty of Control Systems and Robotics Itmo University 49 Kronverksky Pr. St. Petersburg197101 Russia

This paper provides an analysis of the current state-of-The-Art technologies in the field of auto-operators used in production during the electroplating process. General schemes of operations are presented, benefits and drawbacks of each scheme are discussed. The paper discusses an increase in the operating efficiency of the auto-operator in transient conditions (braking and acceleration) by reducing suspension oscillations and provides an example of a similar problem from other industries. In addition to the classification of the auto-operators, three main ways and control methods of the auto-operator of a galvanic line are presented. The main ways of eliminating oscillations during the movement of the auto-operator, as well as the rationale for the choice of adaptive (optimal) control, based on and comparing the basic control algorithms of the robot manipulator, are discussed. The comparative analysis of algorithms used to determine the optimal control has been carried out. Application field of each optimal control method described, moreover advantages and disadvantages as well as implementation methods described. Bellman dynamic programming method was chosen to eliminate oscillations of the suspension with details during the auto-operator transient conditions, the chosen method takes into account all necessary conditions to achieve the desired result. © 2021 Budapest University of Technology and Economics. All rights reserved.

关键词： Dynamic programming

Stable Inversion of Piecewise Affine systems with Application to Feedforward and Iterative Learning control

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arXiv 2024年

作者： Spiegel, Isaac A. Strijbosch, Nard de Rozario, Robin Oomen, Tom Barton, Kira Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Departments of Mechanical Engineering and Robotics University of Michigan Ann ArborMI48109 United States Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands Eindhoven5612 AZ Netherlands Delft Center for Systems and Control Delft University of Technology Delft2628 CD Netherlands

Model inversion is a fundamental technique in feedforward control. Unstable inverse models present a challenge in that useful feedforward control trajectories cannot be generated by directly propagating them. Stable inversion is a process for generating useful trajectories from unstable inverses by handling their stable and unstable modes separately. Piecewise affine (PWA) systems are a popular framework for modeling complicated dynamics. The primary contributions of this article are closed-form inverse formulas for a general class of PWA models, and stable inversion methods for these models. Both contributions leverage closed-form model representations to prove sufficient conditions for solution existence and uniqueness, and to develop solution computation methods. The result is implementable feedforward control synthesis from PWA models with either stable or unstable inverses. In practice, feedforward control alone may yield substantial tracking errors due to mismatch between the known system model and the unknowable complete system physics. Iterative learning control (ILC) is a technique for achieving robustness to model error in feedforward control. To demonstrate the primary contributions’ validity and utility, this article also integrates PWA stable inversion with ILC in simulations based on a physical printhead positioning *** Codes 93C10 (Primary) 93C62 (Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

Renewable Power Generation Maximization for UAVs with Adaptive Sliding Mode control

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Renewable Power Generation Maximization for UAVs with Adapti...

International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Khalid Alfuwail Ali Nasir Md Shafiullah Ayman Abdallah Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC Intelligent Manufacturing and Robotics (IRC-IMR) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Aerospace Engineering Department IRC for Aviation & Space Exploration (IRC-ASE) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The demand for efficient and Fuel consumption alternative renewable energy sources in the aviation sector has led to the exploration of advanced technologies such as Ram Air Turbines (RATs). RATs are critical in applications requiring power generation in emergency scenarios that may happen to un-manned aerial vehicles (UAVs). However, the primary challenge lies in optimizing the performance of the RATs, such as power output and operational stability, under varying and unpredictable wind conditions. Traditional control methods often fail to adapt to these dynamic environments. This paper underscores the necessity of utilizing a recurrent neural network (RNN) to detect uncertain wind turbine dynamics; a sliding mode control rule is created in the suggested controller to monitor the optimal turbine rotation speed. Next, an online update technique is developed to provide real-time weight updates for the RNN, enabling control over maximum power extraction. According to simulation results, the suggested controller outperforms a traditional control approach in tracking the optimal turbine rotation speed and extracting the most wind power from RATs by 13 times, even in severe nonlinearities and system uncertainties.

关键词： Renewable energy sources Uncertainty Recurrent neural networks Stability analysis Real-time systems Hardware Wind turbines Vehicle dynamics Sliding mode control Wind energy conversion

New frontiers of freeway traffic control and estimation

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New frontiers of freeway traffic control and estimation

IEEE Conference on Decision and control

作者： M. L. Delle Monache C. Pasquale M. Barreau R. Stern Department of Civil and Environmental Engineering University of California Berkeley USA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Division of Decision and Control Systems and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Civil Environmental and Geo-Engineering University of Minnesota USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This article provides an overview of the classical and new techniques in traffic flow control and estimations. The overview begins with a description of the most used traffic flow models for estimation and control. Then, it shifts towards using those models for traffic flow estimation using physics-informed machine learning techniques. Lastly, it focuses on traffic flow control describing the most classical techniques and the new advancement in traffic control using autonomous vehicles.

关键词： Actuators Technological innovation Microscopy Estimation Machine learning Traffic control Data models

Optimized 3D Drone Placement and Resource Allocation for LTE-Based M2M Communications 91

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Optimized 3D Drone Placement and Resource Allocation for LTE...

91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Fahim, Ahmed Gadallah, Yasser Robotics Control and Smart Systems American University in Cairo Department of Electronics and Communications Engineering New Cairo11835 Egypt

ISBN: (纸本)9781728152073

The deployment of drone-mounted communication systems has received increasing interest and attention recently as it allows significant improvement to the network access capacity and coverage. Many applications can benefit from such deployments in particular machine-to-machine (M2M) communications. In this study, we propose a technique for the rapid deployment of an LTE drone-mounted base station to serve a group of machine-type-communication devices (MTCDs) that are deployed in disaster situations or within remote applications. The objective is to maximize the number of served MTCDs while meeting their transmission delay requirements. This results in optimizing the 3D placement of the drone base station as well as the allocation of the available communication resources. We evaluated the proposed technique through several simulation experiments. The simulation results demonstrate significant improvement in the aggregate system throughput and the overall system deadline missing performance along with the increased number of served MTCDs compared to other drone placement approaches. © 2020 IEEE.

关键词： Machine-to-machine communication

SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines

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arXiv 2024年

作者： Ponte, Andrea Trizna, Dmitrijs Demetrio, Luca Biggio, Battista Ogbu, Ivan Tesfai Roli, Fabio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Viale Causa 13 Genova16145 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Electrical and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09123 Italy Rina Consulting S.p.A. Via Antonio Cecchi 6 Genova16129 Italy

As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia – which pursues an optimal performances in terms of detection rate and low false alarms – and the requirements of real-world scenarios. In particular, academia focuses on combining static and dynamic analysis within a single or ensemble of models, falling into several pitfalls like (i) firing dynamic analysis without considering the computational burden it requires;(ii) discarding impossible-to-analyze samples;and (iii) analyzing robustness against adversarial attacks without considering that malware detectors are complemented with more non-machine-learning components. Thus, in this paper we bridge these gaps, by investigating the properties of malware detectors built with multiple and different types of analysis. To do so, we develop SLIFER, a Windows malware detection pipeline sequentially leveraging both static and dynamic analysis, interrupting computations as soon as one module triggers an alarm, requiring dynamic analysis only when needed. Contrary to the state of the art, we investigate how to deal with samples that impede analyzes, showing how much they impact performances, concluding that it is better to flag them as legitimate to not drastically increase false alarms. Lastly, we perform a robustness evaluation of SLIFER. Counter-intuitively, the injection of new content is either blocked more by signatures than dynamic analysis, due to byte artifacts created by the attack, or it is able to avoid detection from signatures, as they rely on constraints on file size disrupted by attacks. As far as we know, we are the first to investigate the properties of sequential malware detectors, shedding light on their behavior in real production environment. Copyright © 2024, The Authors. All rights reserved.

关键词： Malware