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Bearing-only formation control under persistence of excitation

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arXiv 2020年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur Nice-Sophia Antipolis France I3S-CNRS Institut Universitaire de France Nice-Sophia Antipolis France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is bearing PE, distributed bearing-only control laws are proposed, which guarantee exponential stabilization of the desired formation only up to a translation vector. The key outcome of this approach relies in exploiting the bearing PE to significantly relax the conditions imposed on the graph topology to ensure exponential stabilization, when compared to the bearing rigidity conditions, and to remove the scale ambiguity introduced by bearing vectors. Simulation results are provided to illustrate the performance of the proposed control method. Copyright © 2020, The Authors. All rights reserved.

关键词： Stabilization

Existence and uniqueness of global Koopman eigenfunctions for stable fixed points and periodic orbits

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arXiv 2019年

作者： Kvalheim, Matthew D. Revzen, Shai Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Ecology and Evolutionary Biology Department Robotics Institute University of Michigan Ann ArborMI48109 United States

We consider C1 dynamical systems having an attracting hyperbolic fixed point or periodic orbit and prove existence and uniqueness results for Ck (actually Clock,α) linearizing semiconjugacies-of which Koopman eigenfunctions are a special case-defined on the entire basin of attraction. Our main results both generalize and sharpen Sternberg's Ck linearization theorem for hyperbolic sinks, and in particular our corollaries include uniqueness statements for Sternberg linearizations and Floquet normal forms. Using our main results we also prove new existence and uniqueness statements for Ck Koopman eigenfunctions, including a complete classification of C∞ eigenfunctions assuming a C∞ dynamical system with semisimple and nonresonant linearization. We give an intrinsic definition of "principal Koopman eigenfunctions" which generalizes the definition of Mohr and Mezić for linear systems, and which includes the notions of "isostables" and "isostable coordinates" appearing in work by Ermentrout, Mauroy, Mezić, Moehlis, Wilson, and others. Our main results yield existence and uniqueness theorems for the principal eigenfunctions and isostable coordinates and also show, e.g., that the (a priori non-unique) "pullback algebra" defined in [MM16b] is unique under certain conditions. We also discuss the limit used to define the "faster" isostable coordinates in [WE18, MWMM19] in light of our main *** Codes 37C05, 37C10, 37C15, 37C27, 34C15, 34C20, 37D99 Copyright © 2019, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Port-Hamiltonian Approach to Neural Network Training

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Port-Hamiltonian Approach to Neural Network Training

IEEE Annual Conference on Decision and Control

作者： Stefano Massaroli Michael Poli Federico Califano Angela Faragasso Jinkyoo Park Atsushi Yamashita Hajime Asama Department of Precision Engineering The University of Tokyo Tokyo Japan Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Faculty of Electrical Engineering Mathematics & Computer Science (EWI) Robotics and Mechatronics (RAM) University of Twente Enschede The Netherlands

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE);however, these networks are still optimized via discrete methods (e.g. gradient descent). In this paper, we explore a different direction: namely, we propose a novel framework for learning in which the parameters themselves are solutions of ODEs. By viewing the optimization process as the evolution of a port-Hamiltonian system, we can ensure convergence to a minimum of the objective function. Numerical experiments have been performed to show the validity and effectiveness of the proposed methods.

关键词： Training Biological neural networks Convergence Mathematical model Optimization Linear programming

Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

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Predictive Inverse Kinematics: optimizing Future Trajectory ...

2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Online multi-object tracking and segmentation with GMPHD filter and mask-based affinity fusion

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arXiv 2020年

作者： Song, Young-Min Yoon, Young-Chul Yoon, Kwangjin Jeon, Moongu Lee, Seong-Whan Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Technology Gwangju61005 Korea Republic of The Robotics Laboratory Hyundai Motor Company Gyeonggi-do 16082 Korea Republic of SI Analytics Co. Ltd. Daejeon34051 Korea Republic of The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2H5 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU-only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets. Copyright © 2020, The Authors. All rights reserved.

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

Motion planning for multi-mobile-manipulator payload transport systems

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arXiv 2019年

作者： Tallamraju, Rahul Salunkhe, Durgesh Haribhau Rajappa, Sujit Ahmad, Aamir Karlapalem, Kamalakar Shah, Suril Vijaykumar Agents and Applied Robotics Group IIIT Hyderabad India Dibris Department University of Genoa Italy Department of Computer Science University of Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany Mechanical Engineering Department IIT Jodhpur India

In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute realtime collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. This approach facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion planning

Deep radiomics for brain tumor detection and classification from multi-sequence mri

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arXiv 2019年

作者： Banerjee, Subhashis Mitra, Sushmita Masulli, Francesco Rovetta, Stefano Indian Statistical Institute Machine Intelligence Unit Kolkata700108 India University of Calcutta Department of Computer Science and Engineering Kolkata700106 India University of Genova Dept of Informatics Bioengineering Robotics and Systems Engineering Genoa16146 Italy

Glioma constitutes 80% of malignant primary brain tumors in adults, and is usually classified as High Grade Glioma (HGG) and Low Grade Glioma (LGG). The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of Deep Convolutional Neural Networks (ConvNets) for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out (LOPO) testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. Results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of 95% for the low/high grade glioma classification problem. A score of 97% is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort towards extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of 7% on the grading performance of ConvNets and 9% on the predi

关键词： Magnetic resonance imaging

Perfect match: A simple method for learning representations for counterfactual inference with neural networks

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arXiv 2018年

作者： Schwab, Patrick Linhardt, Lorenz Karlen, Walter Institute of Robotics and Intelligent Systems Eth Zurich Department of Computer Science Eth Zurich

Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment t1?". However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments. Copyright © 2018, The Authors. All rights reserved.

关键词： Patient treatment