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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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Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments

arXiv

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arXiv 2023年

作者： Bukhari, Syed Tanweer Shah Anima, Bashira Akter Feil-Seifer, David Qazi, Wajahat Mahmood Cognitive Robotics Group Faculty of IT and CS University of Central Punjab Lahore Pakistan Intelligent Machines & Robotics Group Department of Computer Science COMSATS University Islamabad Lahore Campus Pakistan Socially Assistive Robotics Group Department of Computer Science & Engineering University of Nevada Reno RenoNV89557 United States

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying "what to do" in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make "tea" and "sandwich". We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says "I am thirsty" or "It is cold outside" the robot will start to perform the tea-making skill. In contrast, if the person says, "I am hungry" or "I need something to eat", the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. © 2023, CC BY.

关键词： Anthropomorphic robots

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Gait Analysis and Control of Rimless Wheel Rovers for Planetary Exploration

Gait Analysis and Control of Rimless Wheel Rovers for Planet...

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Space robotics (iSpaRo), International Conference on

作者： Dennis Schlee Shubham Vyas Rauĺ Domínguez Frank Kirchner DFKI GmbH - Robotics Innovation Center Bremen Germany Intelligent Autonomous Systems Group Technical University of Darmstadt Darmstadt Germany Department of Mathematics and Computer Science AG Robotics University of Bremen Bremen Germany

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

Rimless wheeled robots are a hybrid variant of traditionally wheeled and legged robots. These hybrid systems combine the benefits of both approaches, such as power efficiency and good terrain traversability. However, the simplicity of the design of the rimless wheel has the disadvantage of limiting the flexibility of foot placement and the impacts caused on the body when navigating rigid surfaces. In this work, we focus on analyzing the effects of different gaits on the robot’s center body. For that, a novel gait classification approach for a rimless wheeled system is introduced and the effects of selected gaits are evaluated in a simulation using a simplified robot model. The analysis of simulation results shows that the chosen gait has a strong effect on the vertical motion of the robot’s center body.

关键词： Legged locomotion Limiting Navigation Simulation Wheels Aerospace electronics Hybrid power systems

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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FeCAM: exploiting the heterogeneity of class distributions in exemplar-free continual learning 23

FeCAM: exploiting the heterogeneity of class distributions i...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dipam Goswami Yuyang Liu Bartłomiej Twardowski Joost van de Weijer Department of Computer Science Universitat Autònoma de Barcelona and Computer Vision Center Barcelona University of Chinese Academy of Sciences and State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences and Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Department of Computer Science Universitat Autònoma de Barcelona and Computer Vision Center Barcelona and IDEAS-NCBR

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In this paper, we explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes. In an analysis of the feature distributions of classes, we show that classification based on Euclidean metrics is successful for jointly trained features. However, when learning from non-stationary data, we observe that the Euclidean metric is suboptimal and that feature distributions are heterogeneous. To address this challenge, we revisit the anisotropic Mahalanobis distance for CIL. In addition, we empirically show that modeling the feature covariance relations is better than previous attempts at sampling features from normal distributions and training a linear classifier. Unlike existing methods, our approach generalizes to both many- and few-shot CIL settings, as well as to domain-incremental settings. Interestingly, without updating the backbone network, our method obtains state-of-the-art results on several standard continual learning benchmarks. Code is available at https://***/dipamgoswami/FeCAM.

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