Probabilistic Approaches To Robotic Perception
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Probabilistic Approaches To Robotic Perception
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Author : João Filipe Ferreira
language : en
Publisher: Springer
Release Date : 2013-08-30
Probabilistic Approaches To Robotic Perception written by João Filipe Ferreira and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-30 with Technology & Engineering categories.
This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing. The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public’s imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited. In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the “irreducible incompleteness of models”.
Probabilistic Robotics
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Author : Sebastian Thrun
language : en
Publisher: MIT Press
Release Date : 2005-08-19
Probabilistic Robotics written by Sebastian Thrun and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-19 with Technology & Engineering categories.
An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Deep Learning For Robot Perception And Cognition
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Author : Alexandros Iosifidis
language : en
Publisher: Academic Press
Release Date : 2022-02-04
Deep Learning For Robot Perception And Cognition written by Alexandros Iosifidis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Technology & Engineering categories.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Robotic Systems Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2020-01-03
Robotic Systems Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-03 with Technology & Engineering categories.
Through expanded intelligence, the use of robotics has fundamentally transformed a variety of fields, including manufacturing, aerospace, medicine, social services, and agriculture. Continued research on robotic design is critical to solving various dynamic obstacles individuals, enterprises, and humanity at large face on a daily basis. Robotic Systems: Concepts, Methodologies, Tools, and Applications is a vital reference source that delves into the current issues, methodologies, and trends relating to advanced robotic technology in the modern world. Highlighting a range of topics such as mechatronics, cybernetics, and human-computer interaction, this multi-volume book is ideally designed for robotics engineers, mechanical engineers, robotics technicians, operators, software engineers, designers, programmers, industry professionals, researchers, students, academicians, and computer practitioners seeking current research on developing innovative ideas for intelligent and autonomous robotics systems.
Towards Autonomous Robotic Systems
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Author : Kaspar Althoefer
language : en
Publisher: Springer
Release Date : 2019-07-16
Towards Autonomous Robotic Systems written by Kaspar Althoefer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 with Computers categories.
The two volumes LNAI 11649 and 11650 constitute the refereed proceedings of the 20th Annual Conference "Towards Autonomous Robotics", TAROS 2019, held in London, UK, in July 2019. The 87 full papers and 12 short papers presented were carefully reviewed and selected from 101 submissions. The papers present and discuss significant findings and advances in autonomous robotics research and applications. They are organized in the following topical sections: robotic grippers and manipulation; soft robotics, sensing and mobile robots; robotic learning, mapping and planning; human-robot interaction; and robotic systems and applications.
Autonomous Robot Vehicles
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Author : Ingemar J. Cox
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Autonomous Robot Vehicles written by Ingemar J. Cox and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Autonomous robot vehicles are vehicles capable of intelligent motion and action without requiring either a guide or teleoperator control. The recent surge of interest in this subject will grow even grow further as their potential applications increase. Autonomous vehicles are currently being studied for use as reconnaissance/exploratory vehicles for planetary exploration, undersea, land and air environments, remote repair and maintenance, material handling systems for offices and factories, and even intelligent wheelchairs for the disabled. This reference is the first to deal directly with the unique and fundamental problems and recent progress associated with autonomous vehicles. The editors have assembled and combined significant material from a multitude of sources, and, in effect, now conviniently provide a coherent organization to a previously scattered and ill-defined field.
Towards Dependable Robotic Perception
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Author : Anna V. Petrovskaya
language : en
Publisher: Stanford University
Release Date : 2011
Towards Dependable Robotic Perception written by Anna V. Petrovskaya and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Reliable perception is required in order for robots to operate safely in unpredictable and complex human environments. However, reliability of perceptual inference algorithms has been poorly studied so far. These algorithms capture uncertain knowledge about the world in the form of probabilistic belief distributions. A number of Monte Carlo and deterministic approaches have been developed, but their efficiency depends on the degree of smoothness of the beliefs. In the real world, the smoothness assumption often fails, leading to unreliable perceptual inference results. Motivated by concrete robotics problems, we propose two novel perceptual inference algorithms that explicitly consider local non-smoothness of beliefs and adapt to it. Both of these algorithms fall into the category of iterative divide-and-conquer methods and hence scale logarithmically with desired accuracy. The first algorithm is termed Scaling Series. It is an iterative Monte Carlo technique coupled with annealing. Local non-smoothness is accounted for by sampling strategy and by annealing schedule. The second algorithm is termed GRAB, which stands for Guaranteed Recursive Adaptive Bounding. GRAB is an iterative adaptive grid algorithm, which relies on bounds. In this case, local non-smoothness is captured in terms of local bounds and grid resolution. Scaling Series works well for beliefs with sharp transitions, but without many discontinuities. GRAB is most appropriate for beliefs with many discontinuities. Both of these algorithms far outperform the prior art in terms of reliability, efficiency, and accuracy. GRAB is also able to guarantee that a quality approximation of the belief is produced. The proposed algorithms are evaluated on a diverse set of real robotics problems: tactile perception, autonomous driving, and mobile manipulation. In tactile perception, we localize objects in 3D starting with very high initial uncertainty and estimating all 6 degrees of freedom. The localization is performed based on tactile sensory data. Using Scaling Series, we obtain highly accurate and reliable results in under 1 second. Improved tactile object localization contributes to manufacturing applications, where tactile perception is widely used for workpiece localization. It also enables robotic applications in situations where vision can be obstructed, such as rescue robotics and underwater robotics. In autonomous driving, we detect and track vehicles in the vicinity of the robot based on 2D and 3D laser range finders. In addition to estimating position and velocity of vehicles, we also model and estimate their geometric shape. The geometric model leads to highly accurate estimates of pose and velocity for each vehicle. It also greatly simplifies association of data, which are often split up into separate clusters due to occlusion. The proposed Scaling Series algorithm greatly improves reliability and ensures that the problem is solved within tight real time constraints of autonomous driving. In mobile manipulation, we achieve highly accurate robot localization based on commonly used 2D laser range finders using the GRAB algorithm. We show that the high accuracy allows robots to navigate in tight spaces and manipulate objects without having to sense them directly. We demonstrate our approach on the example of simultaneous building navigation, door handle manipulation, and door opening. We also propose hybrid environment models, which combine high resolution polygons for objects of interest with low resolution occupancy grid representations for the rest of the environment. High accuracy indoor localization contributes directly to home/office mobile robotics as well as to future robotics applications in construction, inspection, and maintenance of buildings. Based on the success of the proposed perceptual inference algorithms in the concrete robotics problems, it is our hope that this thesis will serve as a starting point for further development of highly reliable perceptual inference methods.
Factor Graphs For Robot Perception
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Author : Frank Dellaert
language : en
Publisher:
Release Date : 2017-08-15
Factor Graphs For Robot Perception written by Frank Dellaert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-15 with Technology & Engineering categories.
Reviews the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are introduced as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them.
Probabilistic Reasoning And Decision Making In Sensory Motor Systems
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Author : Pierre Bessière
language : en
Publisher: Springer
Release Date : 2008-08-27
Probabilistic Reasoning And Decision Making In Sensory Motor Systems written by Pierre Bessière and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-27 with Technology & Engineering categories.
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filtering, modelling and mapping, with applications in a number of different contexts.
Vision Based Mobile Robotics Mobile Robot Localization Using Vision Sensors And Active Probabilistic Approaches
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Author : Emanuele Frontoni
language : en
Publisher: Lulu.com
Release Date : 2012-01-22
Vision Based Mobile Robotics Mobile Robot Localization Using Vision Sensors And Active Probabilistic Approaches written by Emanuele Frontoni and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-22 with Technology & Engineering categories.
The use of vision in mobile robotics in one of the main goal of this thesis. In particular novel appearance based approaches for image matching metric are introduced. These approaches are applied to the problem of mobile robot localization.Similarity measures between robot's views are used in probabilistic methods for robot pose estimation. In this field of probabilistic localization active approach are proposed allowing the robot to faster and better localize. All methods have been extensively tested using a real robot in an indoor environment.Note: the book is the publication of the PhD thesis discussed in Università Politecnica delle Marche, Ancona, Italy in 2006 by Emanuele Frontoni