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Online discovery of AUV control policies to overcome thruster failures

Online discovery of AUV control policies to overcome thruste...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Seyed Reza Ahmadzadeh Matteo Leonetti Arnau Carrera Marc Carreras Petar Kormushev Darwin G. Caldwell Department of Advanced Robotics Istituto Italiano di Tecnologia Genova Italy Department of Computer Science The University of Texas at Austin Austin TX Computer Vision and Robotics Group (VICOROB) University of Girona Girona Spain

We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the AUV. The model is adapted to a new condition when a fault is detected and isolated. Since the approach generates an optimal trajectory, the learned fault-tolerant policy is able to navigate the AUV towards a specified target with minimum cost. Finally, the learned policy is executed on the real robot in a closed-loop using the state feedback of the AUV. Unlike most existing methods which rely on the redundancy of thrusters, our approach is also applicable when the AUV becomes under-actuated in the presence of a fault. To validate the feasibility and efficiency of the presented approach, we evaluate it with three learning algorithms and three policy representations with increasing complexity. The proposed method is tested on a real AUV, Girona500.

关键词： Optimization Trajectory Vectors Vehicles Robots Fault tolerance Fault tolerant systems

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haozhe Ma, Liyan Ying, Shihui Institute of Artificial Intelligence Shanghai University Shanghai200444 China School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368604

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects. © 2024 IEEE.

关键词： Active learning

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Similarity-Based Clustering 1

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丛书名： Lecture Notes in computer science

1000年

作者： Michael Biehl Barbara Hammer Michel Verleysen Thomas Villmann

ISBN: (数字)9783642018053

ISBN: (纸本)9783642018046

Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

关键词： Life sciences, general Computational Biology/Bioinformatics Biomedicine general Data Mining and Knowledge Discovery Information Storage and Retrieval computer Imaging, vision, Pattern Recognition and Graphics

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach 26

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Akter, Mst Shapna Shahriar, Hossain Rahman, Md Abdur Rahman, Muhammad Cuzzocrea, Alfredo University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center for Cybersecurity United States Jahangirnagar University Department of Mathematics Dhaka Savar Bangladesh Clayton State University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9798350359015

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market. © 2023 IEEE.

关键词： Cryptocurrency deep learning ensemble learning time-series forecasting volatility

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Bézier curve based dynamic obstacle avoidance and trajectory learning for autonomous mobile robots

Bézier curve based dynamic obstacle avoidance and trajector...

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2010 10th International Conference on intelligent Systems Design and Applications, ISDA'10

作者： Chaudhry, Tayyab Gulrez, Tauseef Zia, Ali Zaheer, Shyba Intelligent Machines and Robotics Research Group Department of Computer Science COMSATS Institute of Information Technology Lahore M.A. Jinnah Campus Defence Road Lahore Pakistan Department of Electrical Engineering College of Engineering and Applied Sciences Al Ghurair University Dubai United Arab Emirates

ISBN: (纸本)9781424481354

This paper addresses the problem of avoiding dynamic obstacles while following the learned trajectory through non-point based maps directly through laser data. The geometric representation of free configuration area changes while a moving obstacle enters into the safety region of autonomous mobile robot. We have applied the Bézier curve properties to the free configuration eigenspaces to satisfy the dynamic obstacle avoidance path constraints. The algorithm is designed to accurately represent the mobile robot's characteristics while avoiding obstacle such as minimum turning radius. Moreover, we also discuss the obstacle avoided path feasibility as a vectorial combination of free configuration eigen-vectors at discrete time scan-frames to manifest a trajectory, which once followed and mapped onto the two control signals of mobile robot will enable it to build an efficient and accurate online environment map. Preliminary results in Matlab have been shown to validate the idea, while the same has been implemented in Player/stage (robotics real-time software) to analyze the performance of the proposed system. © 2010 IEEE.

关键词： Trajectories

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Process Model Parameterisation in Posegraphs

Process Model Parameterisation in Posegraphs

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American Control Conference

作者： Simon J. Julier Zhaojie Ju Department of Computer Science University College London UK S Intelligent Systems and Biomedical Robotics Group at the University of Portsmouth UK

ISBN: (纸本)9781479901777

Through propagating information over time, process models serve a vital in any multi-time step estimation algorithm. However, they can introduce nonlinearities which can significantly degrade the performance of an estimator. In this paper, we investigate the impact of the parameter-isation of the process model in posegraph-based formulations of filtering and estimation algorithms. Exploiting the flexibility and conditional independence structure of a posegraph, we develop two formulations of a process noise model of a vehicle - one in Euclidean space, the other in polar space. Using moment-matching, we develop exact closed form solutions for the first two moments of a Gaussian distribution propagated through both models. We analyse the effects of both formulations in the context of a Simultaneous Localisation and Mapping (SLAM) problem. We show that, by representing the "arc-like" nature of the prediction error more accurately, the polar form is more accurate, more robust, and is less computationally expensive than the Euclidean form.

关键词： process prediction Euclidean Euclidean procedural model parameterization Gaussian distribution simultaneous localization and mapping Conditional Independence Euclidean space Error of prediction nonlinearity Filtration

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Deep Learning and computer vision Techniques for Estimating Snow Coverage on Roads using Surveillance Cameras

Deep Learning and Computer Vision Techniques for Estimating ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： François-Guillaume Landry Moulay A. Akhloufi Department of Computer Science Perception Robotics and Intelligent Machines Research Group (PRIME) Université de Moncton Moncton NB Canada

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Road surface monitoring in winter conditions is of great importance to ensure the safety of road users. Estimation of snow coverage on roads can be included in intelligent transportation systems to alert drivers or improve snow removal processes. Several models have been proposed for estimating snow coverage using surveillance cameras, but these models have focused on predicting few snow levels, which limits their usefulness in practice. In this paper, we present a model that allows a more granular estimation of the percentage of road surface covered by snow by predicting snow coverage from 0% (no snow) to 100% (fully snow-covered) using increments of 10%. We propose an ensemble learning model combining a deep convolutional neural network (CNN) and a support-vector machine (SVM). The accuracy of our model is similar to the state-of-the-art accuracy despite the higher task complexity associated with the increased granularity of predictions.

关键词： Support vector machines Image segmentation Snow Computational modeling Roads Surveillance Predictive models

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Reports on the 2017 AAAI spring symposium series

Reports on the 2017 AAAI spring symposium series

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作者： Bohg, Jeannette Boix, Xavier Chang, Nancy Chu, Vivian Churchill, Elizabeth F. Fang, Fei Feldman, Jerome González, Avelino J. Kido, Takashi Lawless, William F. Montaña, José L. Ontañón, Santiago Sinapov, Jivko Sofge, Don Steels, Luc Steenson, Molly Wright Takadama, Keiki Yadav, Amulya Max Planck Institute for Intelligent Systems United States Massachusetts Institute of Technology United States Institute of Robotics and Intelligent Machines Georgia Institute of Technology United States Google United States Harvard University United States University of California Berkeley United States Department of Computer Science University of Central Florida United States Preferred Networks in Japan Japan Paine College in Augusta GA United States Department of Mathematics Statistics and Computer Science University of Cantabria Spain Department of Computer Science Drexel University United States Department of Computer Science University of Texas Austin United States Naval Research Laboratory United States Barcelona Spain Carnegie Mellon University School of Design United States University of Electro-Communications Japan Computer Science Department University of Southern California United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27-29, 2017, on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01);Computational Construction Grammar and Natural Language Understanding (SS-17-02);Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03);Designing the User Experience of Ma chine-Learning Systems (SS-17-04);Interactive Multisensory Object Perception for Embodied Agents (SS-17-05);Learning from Ob servation of Humans (SS-17-06);science of Intelligence: Com putational Principles of Natural and Artificial Intelligence (SS-17-07);and Well-Being AI: From Machine Learning to Subjectivity-Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place. © Copyright 2017, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Learning systems

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View-based 3-D object recognition using shock graphs

View-based 3-D object recognition using shock graphs

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International Conference on Pattern Recognition

作者： D. Macrini A. Shokoufandeh S. Dickinson K. Siddiqi S. Zucker Department of Computer Science University of Toronto Canada Department of Mathematics and Computer Science Drexel University USA Centre for Intelligent Machines School of Computer Science McGill University Canada Center for Computational Vision and Control Yale University USA

The shock graph is an emerging shape representation for object recognition, in which a 2-D silhouette is decomposed into a set of qualitative parts, captured in a directed acyclic graph. Although a number of approaches have been proposed for shock graph matching, these approaches do not address the equally important indexing problem. We extend our previous work in both shock graph matching and hierarchical structure indexing to propose the first unified framework for view-based 3-D object recognition using shock graphs. The heart of the framework is an improved spectral characterization of shock graph structure that not only drives a powerful indexing mechanism (to retrieve similar candidates from a large database), but also drives a matching algorithm that can accommodate noise and occlusion. We describe the components of our system and evaluate its performance using both unoccluded and occluded queries. The large set of recognition trials (over 25,000) from a large database (over 1400 views) represents one of the most ambitious shock graph-based recognition experiments conducted to date.

关键词： Object recognition Electric shock Indexing computer science Spatial databases computer vision Shape control Layout mathematics Heart

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The design of a single degree of freedom open-loop spatial mechanism that incorporates geared connections

The design of a single degree of freedom open-loop spatial m...

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2009 ASME International Mechanical Engineering Congress and Exposition, IMECE2009

作者： Bari, Joseph M. Crane III, Carl D. Dooner, David B. Mckinley, Javier Roldan University of Florida Center for Intelligent Machines and Robotics Gainesville FL United States University of Puerto Rico at Mayaguez Department of Mechanical Engineering Mayaguez Puerto Rico LaGuardia Community College C.U.N.Y. Mathematics Engineering and Computer Science United States

ISBN: (纸本)9780791843772

A means has been discovered to apply gear pairing to create a one degree of freedom open-loop spatial mechanism. A specially chosen geometry consisting of three pairs of parallel joint axes is constricted by five sets of gears, three of which are parallel planar, allows for a reconfigurable mechanism that is suited for repetitive tasks. Previous work has examined three-dimensional rigid body guidance in closed-loop geared mechanisms, but has not come to a solution for the open-loop case. Gear pairs are designed based upon a desired position and orientation path for the end effector. Numerical optimization is performed to obtain physically realizable gear profiles. Noncircular gear centrodes must be continuous and smooth as well as mono-directional, that is, gear ratios of a given pair may not switch signs. These constraints eliminate non-realizable or non-optimal gears in favor of simple, more easily produced profiles. Variable parameters include link lengths, joint offsets and twist angles. Numerical examples are presented. Copyright © 2010 by ASME.

关键词： Gears

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