The Experts below are selected from a list of 93405 Experts worldwide ranked by ideXlab platform
Sebastian Thrun - One of the best experts on this subject based on the ideXlab platform.
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conditional particle filters for simultaneous mobile robot localization and people tracking
International Conference on Robotics and Automation, 2002Co-Authors: Michael Montemerlo, Sebastian Thrun, Warren WhittakerAbstract:Presents a Probabilistic Algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called the conditional particle filter, tracks a large distribution of person locations conditioned upon a smaller distribution of robot poses over time. This method is robust to sensor noise, occlusion, and uncertainty in robot localization. In fact, conditional particle filters can accurately track people in situations with global uncertainty over robot pose. The number of samples required by this filter scales linearly with the number of people being tracked, making the Algorithm feasible to implement in real-time in environments with large numbers of people. Experimental results illustrate the accuracy of tracking and model selection, as well as the performance of an active following behavior based on this Algorithm.
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a Probabilistic on line mapping Algorithm for teams of mobile robots
The International Journal of Robotics Research, 2001Co-Authors: Sebastian ThrunAbstract:An efficient Probabilistic Algorithm for the concurrent mapping and localization problem that arises in mobile robotics is presented. The Algorithm addresses the problem in which a team of robots builds a map on-line while simultaneously accommodating errors in the robots’ odometry. At the core of the Algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an on-line Algorithm that can cope with large odometric errors typically found when mapping environments with cycles. The Algorithm can be implemented in a distributed manner on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in three dimensions.
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an online mapping Algorithm for teams of mobile robots
2000Co-Authors: Sebastian ThrunAbstract:Abstract : We propose a new Probabilistic Algorithm for online mapping of unknown environments with teams of robots. At the core of the Algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an online Algorithm that can cope with large odometric errors typically found when mapping an environment with cycles. The Algorithm can be implemented distributedly on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in 3D.
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Collaborative multi-robot localization
Annual German Conference on Artificial Intelligence, 1999Co-Authors: Dieter Fox, Hannes Kruppa, Wolfram Burgard, Sebastian ThrunAbstract:This paper presents a Probabilistic Algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, Probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots. The robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
Dana Petcu - One of the best experts on this subject based on the ideXlab platform.
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DEPAS: a decentralized Probabilistic Algorithm for auto-scaling
Computing, 2012Co-Authors: Nicolò M. Calcavecchia, Bogdan Alexandru Caprarescu, Daniel J. Dubois, Elisabetta Di Nitto, Dana PetcuAbstract:The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers’ solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS , a decentralized Probabilistic auto-scaling Algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our experiments (simulations and real deployments), which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.
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DEPAS: A decentralized Probabilistic Algorithm for auto-scaling
Computing, 2012Co-Authors: Nicolò M. Calcavecchia, Bogdan Alexandru Caprarescu, Daniel J. Dubois, Elisabetta Di Nitto, Dana PetcuAbstract:The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized Probabilistic auto-scaling Algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.
Wa Qiula - One of the best experts on this subject based on the ideXlab platform.
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implementation of ac optimal power flow based financial transmission right auction model under static security constraints
Power system technology, 2010Co-Authors: Wa QiulaAbstract:As a financial instrument for hedging risk, financial transmission right (FTR) has been put into application in some electricity markets. Considering the contingency constraints, this paper proposes a new FTR auction model based on AC optimal power flow (OPF) in which the n-1 static security constraints are included, and the AC-OPF based on Monte Carlo simulation is applied to solve the proposed model. Corresponding contingency set is chosen by Probabilistic Algorithm, and the optimization solution of expectation value and frequency distribution of clearing results is obtained. The proposed method is verified by IEEE 5-bus system and IEEE 30-bus system respectively, calculation results show that the proposed model and method are reasonable and feasible.
Shir Peled - One of the best experts on this subject based on the ideXlab platform.
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tri tri again finding triangles and small subgraphs in a distributed setting
International Symposium on Distributed Computing, 2012Co-Authors: Danny Dolev, Christoph Lenzen, Shir PeledAbstract:Let G=(V,E) be an n-vertex graph and Md a d-vertex graph, for some constant d. Is Md a subgraph of G? We consider this problem in a model where all n processes are connected to all other processes, and each message contains up to $\mathcal{O}(\log n)$ bits. A simple deterministic Algorithm that requires $\mathcal{O}(n^{(d-2)/d}/\log n)$ communication rounds is presented. For the special case that Md is a triangle, we present a Probabilistic Algorithm that requires an expected $\mathcal{O}(n^{1/3}/(t^ {2/3}+1))$ rounds of communication, where t is the number of triangles in the graph, and $\mathcal{O}(\min\{n^{1/3}\log^{2/3}n/(t^ {2/3}+1),n^{1/3}\})$ with high probability. We also present deterministic Algorithms that are specially suited for sparse graphs. In graphs of maximum degree Δ, we can test for arbitrary subgraphs of diameter D in $\mathcal{O}(\Delta^{D+1}/n)$ rounds. For triangles, we devise an Algorithm featuring a round complexity of $\mathcal{O}((A^2\log_{2+n/A^2} n)/n)$, where A denotes the arboricity of G.
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tri tri again finding triangles and small subgraphs in a distributed setting
arXiv: Distributed Parallel and Cluster Computing, 2012Co-Authors: Danny Dolev, Christoph Lenzen, Shir PeledAbstract:Let G = (V,E) be an n-vertex graph and M_d a d-vertex graph, for some constant d. Is M_d a subgraph of G? We consider this problem in a model where all n processes are connected to all other processes, and each message contains up to O(log n) bits. A simple deterministic Algorithm that requires O(n^((d-2)/d) / log n) communication rounds is presented. For the special case that M_d is a triangle, we present a Probabilistic Algorithm that requires an expected O(ceil(n^(1/3) / (t^(2/3) + 1))) rounds of communication, where t is the number of triangles in the graph, and O(min{n^(1/3) log^(2/3) n / (t^(2/3) + 1), n^(1/3)}) with high probability. We also present deterministic Algorithms specially suited for sparse graphs. In any graph of maximum degree Delta, we can test for arbitrary subgraphs of diameter D in O(ceil(Delta^(D+1) / n)) rounds. For triangles, we devise an Algorithm featuring a round complexity of O(A^2 / n + log_(2+n/A^2) n), where A denotes the arboricity of G.
Nicolò M. Calcavecchia - One of the best experts on this subject based on the ideXlab platform.
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DEPAS: a decentralized Probabilistic Algorithm for auto-scaling
Computing, 2012Co-Authors: Nicolò M. Calcavecchia, Bogdan Alexandru Caprarescu, Daniel J. Dubois, Elisabetta Di Nitto, Dana PetcuAbstract:The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers’ solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS , a decentralized Probabilistic auto-scaling Algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our experiments (simulations and real deployments), which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.
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DEPAS: A decentralized Probabilistic Algorithm for auto-scaling
Computing, 2012Co-Authors: Nicolò M. Calcavecchia, Bogdan Alexandru Caprarescu, Daniel J. Dubois, Elisabetta Di Nitto, Dana PetcuAbstract:The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized Probabilistic auto-scaling Algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.
