Relevant bibliographies by topics / Probabilistic Robotics

Academic literature on the topic 'Probabilistic Robotics'

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Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Probabilistic Robotics"

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Thrun, Sebastian. "Probabilistic robotics." Communications of the ACM 45, no. 3 (March 2002): 52–57. http://dx.doi.org/10.1145/504729.504754.

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Martin, Fred, James Dalphond, and Nat Tuck. "Teaching Localization in Probabilistic Robotics." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 3 (October 4, 2021): 2373–74. http://dx.doi.org/10.1609/aaai.v26i3.18955.

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In the field of probabilistic robotics, a central problem is to determine a robot’s state given knowledge of a time series of control commands and sensor readings. The effects of control commands and the behavior of sensor devices are both modeled probabilistically. A variety of methods are available for deriving the robot’s belief state, which is a probabilistic representation of the robot’s true state (which cannot be directly known). This paper presents a series of five weekly assignments to teach this material at the advanced undergraduate/graduate level. The theoretical aspect of the work is reinforced by practical implementation exercises using ROS (Robot Operating System), and the Bilibot, an educational robot platform.
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Paraschos, Alexandros, Christian Daniel, Jan Peters, and Gerhard Neumann. "Using probabilistic movement primitives in robotics." Autonomous Robots 42, no. 3 (July 15, 2017): 529–51. http://dx.doi.org/10.1007/s10514-017-9648-7.

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KIM, Myungsik, Jong-Min KIM, and Kwangsoo KIM. "2P1-J01 RFID based objects positioning using probabilistic geometrical relations(Network Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2P1—J01_1—_2P1—J01_2. http://dx.doi.org/10.1299/jsmermd.2011._2p1-j01_1.

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Watanabe, Keigo, Shoichi Maeyama, Tetsuo Tomizawa, Ryuichi Ueda, and Masahiro Tomono. "Special Issue on Probabilistic Robotics and SLAM." Journal of Robotics and Mechatronics 31, no. 2 (April 20, 2019): 179. http://dx.doi.org/10.20965/jrm.2019.p0179.

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Intelligent mobile robots need self-localization, map generation, and the ability to explore unknown environments autonomously. Probabilistic processing can be applied to overcome the problems of movement uncertainties and measurement errors. Probabilistic robotics and simultaneous localization and mapping (SLAM) technologies are therefore strongly related, and they have been the focus of many studies. As more and more practical applications are found for intelligent mobile robots, such as for autonomous driving and cleaning, the applicability of these techniques has been increasing. In this special issue, we provide a wide variety of very interesting papers ranging from studies and developments in applied SLAM technologies to fundamental theories for SLAM. There are five academic papers, one each on the following topics: first visit navigation, controls for following rescue clues, indoor localization using magnetic field maps, a new solution for self-localization using downhill simplex method, and object detection for long-term map management through image-based learning. In addition, in the next number, there will be a review paper by Tsukuba University’s Prof. Tsubouchi, who is famous for the Tsukuba Challenge and research related to mobile robotics. We editors hope this special issue will help readers to develop mobile robots and use SLAM technologies and probabilistic approaches to produce successful applications.
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Ye, Kangfeng, Ana Cavalcanti, Simon Foster, Alvaro Miyazawa, and Jim Woodcock. "Probabilistic modelling and verification using RoboChart and PRISM." Software and Systems Modeling 21, no. 2 (October 3, 2021): 667–716. http://dx.doi.org/10.1007/s10270-021-00916-8.

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AbstractRoboChart is a timed domain-specific language for robotics, distinctive in its support for automated verification by model checking and theorem proving. Since uncertainty is an essential part of robotic systems, we present here an extension to RoboChart to model uncertainty using probabilism. The extension enriches RoboChart state machines with probability through a new construct: probabilistic junctions as the source of transitions with a probability value. RoboChart has an accompanying tool, called RoboTool, for modelling and verification of functional and real-time behaviour. We present here also an automatic technique, implemented in RoboTool, to transform a RoboChart model into a PRISM model for verification. We have extended the property language of RoboTool so that probabilistic properties expressed in temporal logic can be written using controlled natural language.
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Patle, B. K., D. R. Parhi, A. Jagadeesh, and Sunil Kumar Kashyap. "Probabilistic fuzzy controller based robotics path decision theory." World Journal of Engineering 13, no. 2 (April 8, 2016): 181–92. http://dx.doi.org/10.1108/wje-04-2016-024.

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Purpose This paper aims to comprise the new ideas for the efficient implementation to autonomous mobile robot navigation over the complex environment in presence of static obstacles. Design/methodology/approach The fuzzy decision via probability and its distribution is applied for the generating objective function subject to the robotics path and obstacle avoidance. The present objective function is formed to achieve high level of significance for the real-time obstacle avoidance and the efficiency. Findings The proposed controller makes a robot take its decision effectively in complex environment in a feasible time. In comparison with other AI approaches the proposed controller reflects that the proposed method outperforms in terms of optimal path and quality of solution. The experimental and simulation results are nearly same. Originality/value It has been tested in a complex crowded environment to find shorter path than existed approaches. For the validation, the experimental and simulation result using Matlab Software (R2008) has provided at the end of the paper.
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Damgaard, Malte Rørmose, Rasmus Pedersen, and Thomas Bak. "Study of Variational Inference for Flexible Distributed Probabilistic Robotics." Robotics 11, no. 2 (March 24, 2022): 38. http://dx.doi.org/10.3390/robotics11020038.

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By combining stochastic variational inference with message passing algorithms, we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases, the robots share the operating space and need to cross each other’s paths multiple times without colliding.
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Matsumoto, Yoshio, and Kae Doki. "On special issue “Probabilistic Approach to Robotics”." Journal of the Robotics Society of Japan 29, no. 5 (2011): 403. http://dx.doi.org/10.7210/jrsj.29.403.

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Sturm, J., C. Stachniss, and W. Burgard. "A Probabilistic Framework for Learning Kinematic Models of Articulated Objects." Journal of Artificial Intelligence Research 41 (August 30, 2011): 477–526. http://dx.doi.org/10.1613/jair.3229.

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Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.
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Dissertations / Theses on the topic "Probabilistic Robotics"

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Chechetka, Anton. "Query-Specific Learning and Inference for Probabilistic Graphical Models." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/171.

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In numerous real world applications, from sensor networks to computer vision to natural text processing, one needs to reason about the system in question in the face of uncertainty. A key problem in all those settings is to compute the probability distribution over the variables of interest (the query) given the observed values of other random variables (the evidence). Probabilistic graphical models (PGMs) have become the approach of choice for representing and reasoning with high-dimensional probability distributions. However, for most models capable of accurately representing real-life distributions, inference is fundamentally intractable. As a result, optimally balancing the expressive power and inference complexity of the models, as well as designing better approximate inference algorithms, remain important open problems with potential to significantly improve the quality of answers to probabilistic queries. This thesis contributes algorithms for learning and approximate inference in probabilistic graphical models that improve on the state of the art by emphasizing the computational aspects of inference over the representational properties of the models. Our contributions fall into two categories: learning accurate models where exact inference is tractable and speeding up approximate inference by focusing computation on the query variables and only spending as much effort on the remaining parts of the model as needed to answer the query accurately. First, for a case when the set of evidence variables is not known in advance and a single model is needed that can be used to answer any query well, we propose a polynomial time algorithm for learning the structure of tractable graphical models with quality guarantees, including PAC learnability and graceful degradation guarantees. Ours is the first efficient algorithm to provide this type of guarantees. A key theoretical insight of our approach is a tractable upper bound on the mutual information of arbitrarily large sets of random variables that yields exponential speedups over the exact computation. Second, for a setting where the set of evidence variables is known in advance, we propose an approach for learning tractable models that tailors the structure of the model for the particular value of evidence that become known at test time. By avoiding a commitment to a single tractable structure during learning, we are able to expand the representation power of the model without sacrificing efficient exact inference and parameter learning. We provide a general framework that allows one to leverage existing structure learning algorithms for discovering high-quality evidence-specific structures. Empirically, we demonstrate state of the art accuracy on real-life datasets and an order of magnitude speedup. Finally, for applications where the intractable model structure is a given and approximate inference is needed, we propose a principled way to speed up convergence of belief propagation by focusing the computation on the query variables and away from the variables that are of no direct interest to the user. We demonstrate significant speedups over the state of the art on large-scale relational models. Unlike existing approaches, ours does not involve model simplification, and thus has an advantage of converging to the fixed point of the full model. More generally, we argue that the common approach of concentrating on the structure of representation provided by PGMs, and only structuring the computation as representation allows, is suboptimal because of the fundamental computational problems. It is the computation that eventually yields answers to the queries, so directly focusing on structure of computation is a natural direction for improving the quality of the answers. The results of this thesis are a step towards adapting the structure of computation as a foundation of graphical models.
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Li, Yueqiao. "Incremental high quality probabilistic roadmap construction for robot path planning." Thesis, University of Bedfordshire, 2009. http://hdl.handle.net/10547/134950.

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In robotics, path planning refers to the process of establishing paths for robots to move from initial positions to goal positions without colliding into any obstacle within specified environments. Constructing roadmaps and searching for paths in the roadmaps is one of the most commonly used methodologies adopted in path planning. However, most sampling-based path planners focus on improving the speed of constructing roadmaps without taking into account the quality. Therefore, they often produce poor-quality roadmaps. Poor-quality roadmaps can cause problems, such as time-consuming path searches, poor quality path production, and even failure of the searching. This research aims to develop a novel sampling-based path planning algorithm which is able to incrementally construct high-quality roadmaps while answering path queries for robots with many degrees of freedom. A novel K-order surrounding roadmap (KSR) concept is proposed in this research based on a thorough investigation into the criteria of high-quality roadmaps, including the criteria themselves and the relationships between them. A KSR contains K useful cycles. There exist a value T for which we can say, with confidence, that the KSR is a high quality roadmap when K=T. A new sampling-based path planning algorithm, known as the KSR path planner that is able to construct a roadmap incrementally while answering path queries, is also developed. The KSR path planner can be employed to answer path queries without requiring any pre-processing. The planner grows trees from the initial and goal III configurations of a path query and connects these two trees to obtain a path. The path planner retains useful vertices of the trees and uses these to construct the roadmap and adds useful cycles to the existing roadmap in order to improve the quality. The roadmap constructed can be used to answer further queries. With the KSR path planner algorithm, there is no need to calculate the value of K to construct a high quality roadmap in advance. The quality of the roadmap improves as the KSR path planner answer queries until the roadmap is able to answer any path queries and no further useful cycles can be added into the roadmap. If the number of path queries is infinite, a high quality KSR can be constructed. The novelty of this KSR path planner is twofold. Firstly, it employs a vertex category classifier to understand local environments where roadmap vertices reside. The classifier is developed using a decision tree method. The classifier is able to classify vertices in a roadmap based on the region information stored in the vertices and their neighbours within a certain distance. The region information stored in the vertices is obtained while the edges connecting the vertices are added to the roadmap. Therefore, employing the vertex category classifier does not require much additional execution time. Secondly, the KSR path planner selects suitable developed strategies to prune the existing roadmap and add useful cycles according to the identified local environments where the vertices reside to improve the quality of the existing roadmap. Experimental results show that the KSR path planner can construct a roadmap and improve the quality of the roadmap incrementally while answering path queries until the roadmap can answer all the path queries without any pre-processing stage. The roadmap constructed by the KSR path planner then achieves better quality than the roadmaps constructed by Reconfigurable Random Forest (RRF) path planner and traditional probabilistic roadmap (PRM) path planner.
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Lewis, Amy Jeannette. "Surveying Underwater Shipwrecks with Probabilistic Roadmaps." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2059.

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Almost two thirds of the Earth's surface is covered in ocean, and yet, only about 5% of it is mapped. There are an unknown amount of sunken ships, planes, and other artifacts hidden below the sea. Extensive search via boat and a sonar tow fish following a standard lawnmower pattern is used to identify sites of interest. Then, if a site has been determined to potentially be historically significant, the most common next step is a survey by either a human dive team or remotely operated vehicle. These are time consuming, error prone, and potentially dangerous options, but autonomous underwater vehicles (AUVs) are a possible solution. This thesis introduces a system for automatically generating paths for AUVs to survey and map shipwrecks. Most AUVs include software to set a lawnmower path for a given region of ocean, and individualized paths can be set via specifying GPS encoded nodes for the AUV to pass through. This thesis presents an algorithm for generating an individualized path that permits the AUV, equipped with a camera to "see" all sides of a region of interest (i.e. a shipwreck). This allows the region of interest to be completely documented. Photogrammetry can then be used to reconstruct a three-dimensional model, but a path is needed to do so. Paths are generated by a probabilistic roadmap algorithm that uses a rapidly-exploring random tree to quickly cover the volume of exploration space and generate small maps with good coverage. The roadmap is constructed out of nodes, each having its own weight. The weight of a given node is calculated using an objective function which measures an approximate view coverage by casting rays from the virtual view and intersecting them with the region of interest. In addition, the weight of a node is increased if this node allows the AUV to see a new side of the region of interest. In each iteration of the algorithm, a node to expand off of is selected based off its location in space or its high weight, a new node with a given amount of freedom is generated, and then added to the roadmap. The algorithm has degrees of freedom in position, pitch, and yaw as well as the objective function to encourage the path to see all sides of the region of interest. Once all sides of the region of interest have been viewed, a path is determined to be complete. The algorithm was tested in a virtual world where the virtual camera acted as the AUV. All of the images collected from our automatically generated path were used to create 3D models and point clouds using photogrammetry. To measure the effectiveness of our paths versus the pre-packaged lawnmower paths, the 3D models and point clouds created from our algorithm were compared to those generated from running a standard lawnmower pattern. The paths generated by our algorithm captured images that could be used in a 3D reconstruction which were more detailed and showed better coverage of the region of interest than those from the lawnmower pattern.
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Cummins, Mark. "Probabilistic localization and mapping in appearance space." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:a34370f2-a2a9-40b5-9a2d-1c8c616ff07a.

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This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Abstract Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Abstract Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25 ms by the end of the trajectory.
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Dondrup, Christian. "Human-robot spatial interaction using probabilistic qualitative representations." Thesis, University of Lincoln, 2016. http://eprints.lincoln.ac.uk/28665/.

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Current human-aware navigation approaches use a predominantly metric representation of the interaction which makes them susceptible to changes in the environment. In order to accomplish reliable navigation in ever-changing human populated environments, the presented work aims to abstract from the underlying metric representation by using Qualitative Spatial Relations (QSR), namely the Qualitative Trajectory Calculus (QTC), for Human-Robot Spatial Interaction (HRSI). So far, this form of representing HRSI has been used to analyse different types of interactions online. This work extends this representation to be able to classify the interaction type online using incrementally updated QTC state chains, create a belief about the state of the world, and transform this high-level descriptor into low-level movement commands. By using QSRs the system becomes invariant to change in the environment, which is essential for any form of long-term deployment of a robot, but most importantly also allows the transfer of knowledge between similar encounters in different environments to facilitate interaction learning. To create a robust qualitative representation of the interaction, the essence of the movement of the human in relation to the robot and vice-versa is encoded in two new variants of QTC especially designed for HRSI and evaluated in several user studies. To enable interaction learning and facilitate reasoning, they are employed in a probabilistic framework using Hidden Markov Models (HMMs) for online classiffication and evaluation of their appropriateness for the task of human-aware navigation. In order to create a system for an autonomous robot, a perception pipeline for the detection and tracking of humans in the vicinity of the robot is described which serves as an enabling technology to create incrementally updated QTC state chains in real-time using the robot's sensors. Using this framework, the abstraction and generalisability of the QTC based framework is tested by using data from a different study for the classiffication of automatically generated state chains which shows the benefits of using such a highlevel description language. The detriment of using qualitative states to encode interaction is the severe loss of information that would be necessary to generate behaviour from it. To overcome this issue, so-called Velocity Costmaps are introduced which restrict the sampling space of a reactive local planner to only allow the generation of trajectories that correspond to the desired QTC state. This results in a exible and agile behaviour I generation that is able to produce inherently safe paths. In order to classify the current interaction type online and predict the current state for action selection, the HMMs are evolved into a particle filter especially designed to work with QSRs of any kind. This online belief generation is the basis for a exible action selection process that is based on data acquired using Learning from Demonstration (LfD) to encode human judgement into the used model. Thereby, the generated behaviour is not only sociable but also legible and ensures a high experienced comfort as shown in the experiments conducted. LfD itself is a rather underused approach when it comes to human-aware navigation but is facilitated by the qualitative model and allows exploitation of expert knowledge for model generation. Hence, the presented work bridges the gap between the speed and exibility of a sampling based reactive approach by using the particle filter and fast action selection, and the legibility of deliberative planners by using high-level information based on expert knowledge about the unfolding of an interaction.
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Hoffmann, Jan. "Reactive probabilistic belief modeling for mobile robots." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15731.

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Trotz der Entwicklungen der letzten Jahre kommt es in der Robotik immer noch vor, dass mobile Roboter scheinbar sinnlose Handlungen ausführen. Der Grund für dieses Verhalten ist oftmals, dass sich das interne Weltbild des Roboters stark von der tatsächlichen Situation, in der sich der Roboter befindet, unterscheidet. Die darauf basierende Robotersteuerung wählt infolge dieser Diskrepanz scheinbar sinnlose Handlungen aus. Eine wichtige Ursache von Lokalisierungsfehlern stellen Kollisionen des Roboters mit anderen Robotern oder seiner Umwelt dar. Mit Hilfe eines Hindernismodells wird der Roboter in die Lage versetzt, Hindernisse zu erkennen, sich ihre Position zu merken und Kollisionen zu vermeiden. Ferner wird in dieser Arbeit eine Erweiterung der Bewegungsmodellierung beschrieben, die die Bewegung in Mobilitätszustände untergliedert, die jeweils ein eigenes Bewegungsmodell besitzen und die mit Hilfe von Propriozeption unterschieden werden können. Mit Hilfe der Servo-Motoren des Roboters lässt sich eine Art Propriozeption erzielen: der momentan gewünschte, angesteuerte Gelenkwinkel wird mit dem tatsächlich erreichten, im Servo-Motor gemessenen Winkel verglichen. Dieser "Sinn" erlaubt eine bessere Beschreibung der Roboterbewegung. Verbesserung des Sensormodells wird das bisher wenig untersuchte Konzept der Negativinformation, d.h. das Ausbleiben einer erwarteten Messung, genutzt. Bestehende Lokalisierungsansätze nutzen diese Information nicht, da es viele Gründe für ein Ausbleiben einer erwarteten Messung gibt. Eine genaue Modellierung des Sensors ermöglicht es jedoch, Negativinformation nutzbar zu machen. Eine Weltmodellierung, die Negativinformation verarbeiten kann, ermöglicht eine Lokalisierung des Roboters in Situationen, in denen einzig auf Landmarken basierende Ansätze scheitern.
Despite the dramatic advancements in the field of robotics, robots still tend to exhibit erratic behavior when facing unexpected situations, causing them, for example, to run into walls. This is mainly the result of the robot''s internal world model no longer being an accurate description of the environment and the robot''s localization within the environment. The key challenge explored in this dissertation is the creation of an internal world model for mobile robots that is more robust and accurate in situations where existing approaches exhibit a tendency to fail. First, means to avoid a major source of localization error - collisions - are investigated. Efficient collision avoidance is achieved by creating a model of free space in the direct vicinity of the robot. The model is based on camera images and serves as a short term memory, enabling the robot to avoid obstacles that are out of sight. It allows the robot to efficiently circumnavigate obstacles. The motion model of the robot is enhanced by integrating proprioceptive information. Since the robot lacks sensors dedicated to proprioception, information about the current state and configuration of the robot''s body is generated by comparing control commands and actual motion of individual joints. This enables the robot to detect collisions with other robots or obstacles and is used as additional information for modeling locomotion. In the context of sensing, the notion of negative information is introduced. Negative information marks the ascertained absence of an expected observation in feature-based localization. This information is not used in previous work on localization because of the several reasons for a sensor to miss a feature, even if the object lies within its sensing range. This information can, however, be put to good use by carefully modeling the sensor. Integrating negative information allows the robot to localize in situations where it cannot do so based on landmark observation alone.
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Zhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.

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The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene.
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Fasel, Ian Robert. "Learning real-time object detectors probabilistic generative approaches /." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3216357.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed July 24, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 87-91).
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Soysal, Onur. "A Systematic Study Of Probabilistic Aggregation Strategies In Swarm Robotic Systems." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606460/index.pdf.

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In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performance, as measured by these two metrics, change 1) with transition probabilities, 2) with number of simulation steps, and 3) with arena size, is studied.
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Peynot, Thierry. "Selection et controle de modes de deplacement pour un robot mobile autonome en environnements naturels." Thesis, Institut National Polytechnique de Toulouse, 2006. http://ethesis.inp-toulouse.fr/archive/00000395/.

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Autonomous navigation and locomotion of a mobile robot in natural environments remain a rather open issue. Several functionalities are required to complete the usual perception/decision/action cycle. They can be divided in two main categories : navigation (perception and decision about the movement) and locomotion (movement execution). In order to be able to face the large range of possible situations in natural environments, it is essential to make use of various kinds of complementary functionalities, defining various navigation and locomotion modes. Indeed, a number of navigation and locomotion approaches have been proposed in the literature for the last years, but none can pretend being able to achieve autonomous navigation and locomotion in every situation. Thus, it seems relevant to endow an outdoor mobile robot with several complementary navigation and locomotion modes. Accordingly, the robot must also have means to select the most appropriate mode to apply. This thesis proposes the development of such a navigation/locomotion mode selection system, based on two types of data: an observation of the context to determine in what kind of situation the robot has to achieve its movement and an evaluation of the behavior of the current mode, made by monitors which influence the transitions towards other modes when the behavior of the current one is considered as non satisfying. Hence, this document introduces a probabilistic framework for the estimation of the mode to be applied, some navigation and locomotion modes used, a qualitative terrain representation method (based on the evaluation of a difficulty computed from the placement of the robot's structure on a digital elevation map), and monitors that check the behavior of the modes used (evaluation of rolling locomotion efficiency, robot's attitude and configuration watching. . .). Some experimental results obtained with those elements integrated on board two different outdoor robots are presented and discussed.
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Books on the topic "Probabilistic Robotics"

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Wolfram, Burgard, and Fox Dieter, eds. Probabilistic robotics. Cambridge, Mass: MIT Press, 2005.

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Thrun, Sebastian. Probabilistic robotics. Cambridge, MA: MIT Press, 2005.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Pierre, Bessière, Laugier Christian, and Siegwart Roland, eds. Probabilistic reasoning and decision making in sensory-motor systems. Berlin: Springer, 2008.

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Ma, Zongmin. Advances in Probabilistic Databases for Uncertain Information Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Ferreira, João Filipe, and Jorge Miranda Dias. Probabilistic Approaches to Robotic Perception. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02006-8.

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Kucner, Tomasz Piotr, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, and Chittaranjan Srinivas Swaminathan. Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41808-3.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37160-8.

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Burgard, Wolfram, Dieter Fox, Sebastian Thrun, and Ronald C. Arkin. Probabilistic Robotics. MIT Press, 2005.

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Burgard, Wolfram, Dieter Fox, and Sebastian Thrun. Probabilistic Robotics. MIT Press, 2005.

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Book chapters on the topic "Probabilistic Robotics"

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Govea, Alejandro Dizan Vasquez. "Probabilistic Models." In Springer Tracts in Advanced Robotics, 11–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13642-9_2.

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Ferreira, João Filipe, and Jorge Dias. "Probabilistic Learning." In Springer Tracts in Advanced Robotics, 147–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02006-8_6.

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Bayazıt, O. Burçhan, Jyh-Ming Lien, and Nancy M. Amato. "Swarming Behavior Using Probabilistic Roadmap Techniques." In Swarm Robotics, 112–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30552-1_10.

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Hsu, David, Jean-Claude Latombe, and Hanna Kurniawati. "On the Probabilistic Foundations of Probabilistic Roadmap Planning." In Springer Tracts in Advanced Robotics, 83–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48113-3_8.

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Paus, Fabian, and Tamim Asfour. "Probabilistic Representation of Objects and Their Support Relations." In Experimental Robotics, 510–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71151-1_45.

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Rummelhard, Lukas, Amaury Nègre, Mathias Perrollaz, and Christian Laugier. "Probabilistic Grid-Based Collision Risk Prediction for Driving Application." In Experimental Robotics, 821–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23778-7_54.

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Mari, Jean-François, and René Schott. "Some Applications in Robotics." In Probabilistic and Statistical Methods in Computer Science, 177–204. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6280-8_5.

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Lerman, Kristina, Alcherio Martinoli, and Aram Galstyan. "A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems." In Swarm Robotics, 143–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30552-1_12.

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Kudriashov, Andrii, Tomasz Buratowski, Mariusz Giergiel, and Piotr Małka. "SLAM as Probabilistic Robotics Framework Approach." In Mechanisms and Machine Science, 39–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48981-6_3.

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Canal, Gerard, Michael Cashmore, Senka Krivić, Guillem Alenyà, Daniele Magazzeni, and Carme Torras. "Probabilistic Planning for Robotics with ROSPlan." In Towards Autonomous Robotic Systems, 236–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23807-0_20.

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Conference papers on the topic "Probabilistic Robotics"

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Lukac, Martin, and Michitaka Kameyama. "Emotion-aware probabilistic robotics." In 2010 2nd International Symposium on Aware Computing (ISAC). IEEE, 2010. http://dx.doi.org/10.1109/isac.2010.5670464.

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Chantler, M. J. "Probabilistic sensing for underwater robotics." In Second International Conference on `Intelligent Systems Engineering'. IEE, 1994. http://dx.doi.org/10.1049/cp:19940647.

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Balaram, J. "Probabilistic Methods For Robot Motion Determination." In 1988 Robotics Conferences, edited by David P. Casasent. SPIE, 1989. http://dx.doi.org/10.1117/12.960327.

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Toussaint, Marc, and Christian Goerick. "Probabilistic inference for structured planning in robotics." In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iros.2007.4399296.

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Kolling, A., and S. Carpin. "Probabilistic Graph-Clear." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152673.

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Huang, A., and S. Teller. "Probabilistic Lane Estimation using Basis Curves." In Robotics: Science and Systems 2010. Robotics: Science and Systems Foundation, 2010. http://dx.doi.org/10.15607/rss.2010.vi.004.

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Posner, Ingmar, Mark Cummins, and Paul Newman. "Fast Probabilistic Labeling of City Maps." In Robotics: Science and Systems 2008. Robotics: Science and Systems Foundation, 2008. http://dx.doi.org/10.15607/rss.2008.iv.003.

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Chi-Hao Lin and Chieh-Chih Wang. "Probabilistic structure from sound and probabilistic sound source localization." In 2008 IEEE Workshop on Advanced robotics and Its Social Impacts (ARSO). IEEE, 2008. http://dx.doi.org/10.1109/arso.2008.4653584.

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Lomuscio, Alessio, and Edoardo Pirovano. "Verifying Fault-Tolerance in Probabilistic Swarm Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/46.

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Abstract:
We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.
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Matthies, Larry, and Alberto Elfes. "Probabilistic Estimation Mechanisms And Tesselated Representations For Sensor Fusion." In 1988 Robotics Conferences, edited by Paul S. Schenker. SPIE, 1989. http://dx.doi.org/10.1117/12.948906.

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Reports on the topic "Probabilistic Robotics"

1

Atrash, Amin, and Sven Koenig. Probabilistic Planning for Behavior-Based Robots. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada443594.

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