IRIS Pol. Torinohttps://iris.polito.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Mon, 24 Feb 2020 06:02:40 GMT2020-02-24T06:02:40Z10341Cooperative robotic teams for supervision and management of large logistic spaces: methodology and applicationshttp://hdl.handle.net/11583/2374782Titolo: Cooperative robotic teams for supervision and management of large logistic spaces: methodology and applications
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23747822010-01-01T00:00:00ZAn application of Kullback-Leibler divergence to active SLAM and exploration with particle filtershttp://hdl.handle.net/11583/2374783Titolo: An application of Kullback-Leibler divergence to active SLAM and exploration with particle filters
Abstract: Autonomous exploration under uncertain robot position requires the robot to plan a suitable motion policy in order to visit unknown areas while minimizing the uncertainty on its pose. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected gain from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). The technique is shown to enhance robot awareness in detecting loop closing occasions, which are often missed when using other state-of-the-art approaches. Results of extensive tests are reported to support our claims.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23747832010-01-01T00:00:00ZActive SLAM and exploration with particle filters using Kullback-Leibler divergencehttp://hdl.handle.net/11583/2517485Titolo: Active SLAM and exploration with particle filters using Kullback-Leibler divergence
Abstract: Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenario and satisfying given constraints on the admissible uncertainty in map estimation. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration, has received a large attention from the robotic community for its relevance in mobile robotics applications.
In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based
SLAM posterior approximation. This metric is then applied in the definition of the expected information from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of comparing the proposed approach with
the state-of-the-art techniques and to evaluate the maturity of truly autonomous navigation systems based on particle filtering.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25174852014-01-01T00:00:00ZDistributed Random Convex Programming via Constraints Consensushttp://hdl.handle.net/11583/2521523Titolo: Distributed Random Convex Programming via Constraints Consensus
Abstract: This paper discusses distributed approaches for the solution of random convex programs (RCPs). RCPs are convex optimization problems with a (usually large) number N of randomly extracted constraints; they arise in several application areas, especially in the context of decision-making under uncertainty, see [2, 3]. We here consider a setup in which instances of the random constraints (the scenario) are not held by a single centralized processing unit, but are instead distributed among different nodes of a network. Each node “sees” only a small subset of the constraints, and may communicate with neighbors. The objective is to make all nodes converge to the same solution as the centralized RCP problem. To this end, we develop two distributed algorithms that are variants of the constraints consensus algorithm [4, 5]: the active constraints consensus (ACC) algorithm, and the vertex constraints consensus (VCC) algorithm. We show that the ACC algorithm computes the overall optimal solution in finite time, and with almost surely bounded communication at each iteration of the algorithm. The VCC algorithm is instead tailored for the special case in which the constraint functions are convex also w.r.t. the uncertain parameters, and it computes the solution in a number of iterations bounded by the diameter of the communication graph. We further devise a variant of the VCC algorithm, namely quantized vertex constraints consensus (qVCC), to cope with the case in which communication bandwidth among processors is bounded. We discuss several applications of the proposed distributed techniques, including estimation, classification, and random model predictive control, and we present a numerical analysis of the performance of the proposed methods. As a complementary numerical result, we show that the parallel computation of the scenario solution using the ACC algorithm significantly outperforms its centralized equivalent.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25215232014-01-01T00:00:00ZSupervision and monitoring of logistic spaces by a cooperative robot team: methodologies, problems, and solutionshttp://hdl.handle.net/11583/2541288Titolo: Supervision and monitoring of logistic spaces by a cooperative robot team: methodologies, problems, and solutions
Abstract: Mobile robots can be employed in the logistic field to efficiently perform common tasks, like building and updating maps of indoor and outdoor logistic spaces, locating specific goods on the map, tracing the product flow in the area, while preserving situational awareness and safety of the environment. This paper reports and discusses the main results of the MACP4Log (Mobile Autonomous and Cooperating robotic Platforms for supervision and monitoring of large LOGistic surfaces) research project, aimed at the study and development of a set of algorithms and services, enabling autonomous navigation of a team of mobile robots in
large logistic spaces, and exploiting cooperation, through communication with a supervisor and among the robotic platforms. Although the main services required for the robots coincide with the most common issues of mobile robotics (i.e., localization, mapping, SLAM and exploration), the particular characteristics of the logistic spaces introduce specific problems (e.g., related to a high symmetry of the environment and/or to its variability), which must be properly taken into account. The paper discusses in detail such problems, summarizing the main results achieved both from the methodological and the experimental standpoint, and is completed by the description of the general functional architecture of the whole system, including navigation, logistic, and monitoring services.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25412882014-01-01T00:00:00ZA comparative study on active SLAM and autonomous exploration with particle filtershttp://hdl.handle.net/11583/2422944Titolo: A comparative study on active SLAM and autonomous exploration with particle filters
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/11583/24229442011-01-01T00:00:00ZA distributed Gauss-Newton approach for range-based localization of multi agent formationshttp://hdl.handle.net/11583/2381230Titolo: A distributed Gauss-Newton approach for range-based localization of multi agent formations
Abstract: We propose a distributed technique for estimating position of nodes in a networked system from pairwise distance measurements. The localization problem is firstly formulated as an unconstrained optimization problem and the well-known Gauss-Newton centralized approach for obtaining a local solution is reviewed. Then, a distributed Gauss-Newton approach is presented, which is proved to converge to the same solution as its centralized counterpart, under an hypothesis of network connectivity. Localization performance and computational effort of the described approach are evaluated through numerical examples. The distributed solution allows each agent to autonomously compute its position estimate, exchanging information only with its neighbors, without need of communicating with a central station. Furthermore, when the initial guess for optimization is shared among the nodes, each node may retrieve the whole network configuration (i.e., its own position and the positions of all the other nodes in the network), exploiting local measurements into a global representation.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23812302010-01-01T00:00:00ZAn Application of Omnidirectional Vision to Grid-based SLAM in Indoor Environmentshttp://hdl.handle.net/11583/2352679Titolo: An Application of Omnidirectional Vision to Grid-based SLAM in Indoor Environments
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23526792010-01-01T00:00:00ZOn registration of uncertain three-dimensional vectors withapplication to roboticshttp://hdl.handle.net/11583/2422943Titolo: On registration of uncertain three-dimensional vectors withapplication to robotics
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/11583/24229432011-01-01T00:00:00ZA distributed gradient method for localization of formations using relative range measurementshttp://hdl.handle.net/11583/2381229Titolo: A distributed gradient method for localization of formations using relative range measurements
Abstract: Several applications of networked systems require nodes to have precise knowledge of their geometric position. A common setup is the case in which each node is capable of measuring distances with respect to a subset of mates and has to estimate its position in a given reference frame. Most of the state-of-the-art algorithms for network localization presuppose a central unit, capable of collecting agents' measurements and retrieving the configuration of the whole network. In this paper, we explore a decentralized approach to localization based on a distributed implementation of a gradient method with Barzilai-Borwein stepsizes. The advantage of this approach is that each agent may autonomously compute its position estimate, exchanging information only with its neighbors, without need of communicating with a central station and without needing complete knowledge of the network structure. This decentralized scheme is proved to converge, under an hypothesis of network connectivity, to the same solution as its centralized counterpart. Computational performance and scalability of the described approach are also illustrated through numerical experiments.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23812292010-01-01T00:00:00Z