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Coordinated multi-robot exploration through unsupervised clustering of unknown space

Coordinated multi-robot exploration through unsupervised clu...

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： A. Solanas M.A. Garcia Intelligent Robotics and Computer Vision Group/Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper proposes a new coordination algorithm for efficiently exploring an unknown environment with a team of mobile robots. The proposed technique subsequently applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the remaining unknown space into as many disjoint regions as available robots. Each robot is primarily responsible for exploring its assigned region and can help other robots on its way through. Unknown space is dynamically repartitioned as new areas are discovered by the team, balancing thus the overall workload among team members and naturally leading to greater dispersion over the environment and thus faster broad coverage than with previous greedy-like approaches, which guide robots based on maximum profit strategies that simply trade off between distance to the closest frontiers and amount of unknown cells likely to be discovered from them.

关键词： Robot kinematics Orbital robotics Robot sensing systems Mobile robots Space technology intelligent robots Layout computer science mathematics Clustering algorithms

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Improving texture pattern recognition by integration of multiple texture feature extraction methods

Improving texture pattern recognition by integration of mult...

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International Conference on Pattern Recognition

作者： M.A. Garcia D. Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. Experimental results with textured images of outdoor scenes show that the proposed technique yields lower classification errors than widely recognized texture classifiers based on specific families of texture methods.

关键词： Pattern recognition Feature extraction Pixel Image recognition intelligent robots Image segmentation Robot vision systems computer vision computer science mathematics

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Multi-modal visual attention for robotics active vision systems - A reference architecture

Multi-modal visual attention for robotics active vision syst...

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AISB 2011 Symposium on Architectures for Active vision

作者： Hülse, Martin McBride, Sebastian Lee, Mark Department of Computer Science Intelligent Robotics Group Aberywtwyth University SY23 3DB United Kingdom

ISBN: (纸本)9781908187000

This work introduces an architecture for a robotic active vision system equipped with a manipulator that is able to integrate visual and non-visual (tactile) sensorimotor experiences. Inspired by the human vision system, we have implemented a strict separation of object location (where-data) and object features (what-data) in the visual data stream. This separation of what- and where-data has computational advantage but requires sequential fixation of visual cues in order to create and update a coherent view of the world. Hence, visual attention mechanisms must be put in place to decide which is the most task-relevant cue to fixate next. Regarding object manipulation many task relevant object properties (e.g. tactile feedback) are not necessarily related to visual features. Therefore, it is important that non-visual object features can influence visual *** present and demonstrate visual attention mechanisms for an active vision system that are modulated by visual and non-visual object features.

关键词： Manipulators

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The logic of robotics inspired biology

The logic of robotics inspired biology

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International Symposium on AI Inspired Biology, AIIB 2010 - A Symposium at the AISB 2010 Convention

作者： Hülse, Martin Lee, Mark Intelligent Robotics Group Department of Computer Science Aberywtwyth University SY23 3DB United Kingdom

ISBN: (纸本)1902956923

Biologically inspired robotics is a well known approach for the design of autonomous intelligent robot systems. Very often it is assumed that biologically inspired models successfully implemented on robots offer new scientific knowledge for biology too. In other words, robots experiments serving as a replacement for the biological system under investigation are assumed to provide new scientific knowledge for biology. This article is a critical investigation of this assumption. We begin by clarifying what we mean by "new scientific knowledge." Following Karl Popper's work the The Logic of Scientific Discovery we conclude that in general robotic experiments serving as replacement for biological systems can never directly deliver any new scientific knowledge for biology. We further argue that there is no formal guideline which defines the level of "biological plausibility" for biologically inspired robot implementations. Therefore, there is no reason to prefer some kind of robotic setup before others. Any claimed relevance for biology, however, is only justified if results from robotic experiments are translated back into new models and hypotheses amenable to experimental tests within the domain of biology. This translation "back" into biology is very often missing and we will discuss popular robotics frameworks in the context of Brain Research, Cognitive science and Developmental robotics in order to highlight this issue. Nonetheless, such frameworks are valuable and important, like pure mathematics, because they might lead to new formalisms and methods which in future might be essential for gaining new scientific knowledge if applied in biology. No one can tell, if and which of the current robotics frameworks will provide these new scientific tools. What we can already say-the main message of this article-is that robot systems serving as a replacement for biological systems won't be sufficient for the test of biological models, i.e. gaining new scientific knowledge i

关键词： intelligent robots

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On adapting the tensor voting framework to robust color image denoising

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13th International Conference on computer Analysis of Images and Patterns, CAIP 2009

作者： Moreno, Rodrigo Garcia, Miguel Angel Puig, Domenec Julià, Carme Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Av. Països Catalans 26 Tarragona 43007 Spain Department of Informatics Engineering Autonomous University of Madrid Cra. Colmenar Viejo Km 15 Madrid 28049 Spain

ISBN: (纸本)3642037666

This paper presents an adaptation of the tensor voting framework for color image denoising, while preserving edges. Tensors are used in order to encode the CIELAB color channels, the uniformity and the edginess of image pixels. A specific voting process is proposed in order to propagate color from a pixel to its neighbors by considering the distance between pixels, the perceptual color difference (by using an optimized version of CIEDE2000), a uniformity measurement and the likelihood of the pixels being impulse noise. The original colors are corrected with those encoded by the tensors obtained after the voting process. Peak to noise ratios and visual inspection show that the proposed methodology has a better performance than state-of-the-art techniques. © 2009 Springer Berlin Heidelberg.

关键词： Pixels

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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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作者： Macrini, Diego Shokoufandeh, Ali Dickinson, Sven Siddiqi, Kaleem Zucker, Steven Department of Computer Science University of Toronto Department of Mathematics and Computer Science Drexel University Centre for Intelligent Machines School of Computer Science McGill University Center for Computational Vision and Control Yale University

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. © 2002 IEEE.

关键词： Object recognition

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Predicting grasp inertia with a geometric model

Predicting grasp inertia with a geometric model

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2005 Australian Conference on robotics and Automation, ACRA 2005

作者： Kowadlo, Gideon Friedman, Jason Flash, Tamar Intelligent Robotics Research Centre Monash University Clayton VIC Australia Department of Applied Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)0958758379

Controlling fnger impedance is critical for successful grasping. Understanding how humans achieve this is of great interest for learning about human motor control, as well as for applications in robotic grasping. There have been a number of studies on finger impedance in both the robotics and biological fields. They almost exclusively consider only stiffness and viscosity. However, inertia may play an important role in certain grasps, and is important for calculation of the other impedance properties. This paper reports current progress of a project to create a geometric model of the hand for predicting hand/grasp inertia at different configurations (sensed by a glove that measures joint angles) during a variety of tasks.

关键词： robotics

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Spectral methods for view-based 3-D object recognition using silhouettes 9th

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Joint IAPR 9th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2002

作者： Macrini, Diego Shokoufandeh, Ali Dickinson, Sven Siddiqi, Kaleem Zucker, Steven Department of Computer Science University of Toronto Canada Department of Mathematics and Computer Science Drexel University United States Department of Computer Science University of Toronto Canada Centre for Intelligent Machines School of Computer Science McGill University Canada Center for Computational Vision and Control Yale University United States

ISBN: (纸本)3540440119

The shockg raph 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 shockgrap h matching, these approaches do not address the equally important indexing problem. We extend our previous workin both shockgrap h matching and hierarchical structure indexing to propose the first unified frameworkf or view-based 3-D object recognition using shockgrap hs. The heart of the framework is an improved spectral characterization of shockgrap h 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 shockg raph-based recognition experiments conducted to date. This paper represents an expanded version of [12]. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Directed graphs

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

引用

International Conference on computer vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Ridge polynomial neural network with error feedback for recursive multi-step forecast strategy: A case study of carbon dioxide emissions forecasting 2

Ridge polynomial neural network with error feedback for recu...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NN s with error feedbacks appears when there is a need for recursive multi-step forecast strategy because the observed values must be known in order to calculate network errors. This paper proposes to use the last calculated error after finishing training NNs with error feedbacks because the observed values are unknown. This last calculated error is used as a fixed value when producing forecasts using recursive multi-step forecast strategy. For that, this paper investigated this simple solution with a NN with error feedback called the ridge polynomial neural network with error feedback (RPNN-EF). Carbon dioxide emissions for three countries in the organization of the petroleum exporting countries (OPEC) were used in this investigation. The forecasting accuracy of RPNN-EF was compared with seven forecasting methods. According to the obtained results, on average, the proposed solution produces reasonable forecasts compared to the seven forecasting methods. Therefore, this solution can be suggested for NNs with error feedbacks for recursive multi-step forecast strategy. © 2020 IEEE.

关键词： Polynomials

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