IRIS Pol. Torinohttps://iris.polito.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Sat, 14 Dec 2019 02:03:34 GMT2019-12-14T02:03:34Z10341An 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/11583/23526792010-01-01T00:00:00ZPosition estimation from relative distance measurements in multi-agents formationshttp://hdl.handle.net/11583/2381228Titolo: Position estimation from relative distance measurements in multi-agents formations
Abstract: The problem of reconstructing the geometric position of nodes in a networked formation from inter-nodal distance measurements is a complex computational task that involves the minimization of a non-convex and highly multi-modal cost criterion. In this paper, we examine three numerical techniques for attacking this problem, namely an iterative Least-Squares (LS) approach, a Trust-Region (TR) approach, and a Global Continuation (GC) technique based on iterative smoothing. The implementation details of the three methods are discussed in the paper, and extensive numerical simulations are performed in order to highlight the complementary properties of these methods in terms of required computational effort and ability to achieve global convergence.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23812282010-01-01T00:00:00ZA Distributed Technique for Localization of Agent Formations from Relative Range Measurementshttp://hdl.handle.net/11583/2484595Titolo: A Distributed Technique for Localization of Agent Formations from Relative Range Measurements
Abstract: Autonomous agents deployed or moving on land for the purpose of carrying out coordinated tasks need to have good knowledge of their absolute or relative position. For large formations it is often impractical to equip each agent with an absolute sensor such as GPS, whereas relative range sensors measuring inter-agent distances are cheap and commonly available. In this setting, the paper considers the problem of autonomous, distributed estimation of the position of each agent in a networked formation, using noisy measurements of inter- agent distances. The underlying geometrical problem has been studied quite extensively in various fields, ranging from molecular biology to robotics, and it is known to lead to a hard non-convex optimization problem. Centralized algorithms do exist that work reasonably well in finding local or global minimizers for this problem (e.g. semidefinite programming relaxations). Here, we explore a fully decentralized approach for localization from range measurements, and we propose a computational scheme based on a distributed gradient algorithm with Barzilai-Borwein stepsizes. The advantage of this distributed 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.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11583/24845952012-01-01T00:00:00ZDistributed Centroid Estimation from Noisy Relative Measurementshttp://hdl.handle.net/11583/2484597Titolo: Distributed Centroid Estimation from Noisy Relative Measurements
Abstract: We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisyrelativemeasurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents’ positions are estimated with respect to the anchor. We show that our centroid-based algorithm converges to the optimal solution, and such a centroid-based representation produces results that are more accurate than anchor-based ones, irrespective of the selected anchor.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11583/24845972012-01-01T00:00:00ZA Distributed Algorithm for Random Convex Programminghttp://hdl.handle.net/11583/2462625Titolo: A Distributed Algorithm for Random Convex Programming
Abstract: We study a distributed approach for solving random convex programs (RCP) for the case in which problem constraints are distributed among nodes in a processor network. We devise a distributed algorithm that allows network nodes to reach consensus on problem solution by exchanging a local set of constraints at each iteration. We prove that the algorithm assures finite-time convergence to problem solution and we provide explicit bounds on the maximum number of constraints to be exchanged among nodes at each communication round. Numerical experiments confirm the theoretical derivation and show that a parallel implementation of the proposed approach speeds-up the solution of the RCP with respect to centralized computation.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/11583/24626252011-01-01T00:00:00ZSTEPS: PCS results on 1st working prototypehttp://hdl.handle.net/11583/2422945Titolo: STEPS: PCS results on 1st working prototype
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/24229452010-01-01T00:00:00ZRao-Blackwellized Particle Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communicationhttp://hdl.handle.net/11583/2363830Titolo: Rao-Blackwellized Particle Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communication
Abstract: Multi robot systems are envisioned to play an important role in many robotic applications. A main prerequisite for a team deployed in a wide unknown area is the capability of autonomously navigate, exploiting the information acquired through the on-line estimation of both robot poses and surrounding environment model, according to Simultaneous Localization And Mapping (SLAM) framework. As team coordination is improved, distributed techniques for filtering are required in order to enhance autonomous exploration and large scale SLAM increasing both efficiency and robustness of operation. Although Rao-Blackwellized Particle Filters (RBPF) have been demonstrated to be an effective solution to the problem of single robot SLAM, few extensions to teams of robots exist, and these approaches are characterized by strict assumptions on both communication bandwidth and prior knowledge on relative poses of the teammates. In the present paper we address the problem of multi robot SLAM in the case of limited communication and unknown relative initial poses. Starting from the well established single robot RBPF-SLAM, we propose a simple technique which jointly estimates SLAM posterior of the robots by fusing the prioceptive and the eteroceptive information acquired by each teammate. The approach intrinsically reduces the amount of data to be exchanged among the robots, while taking into account the uncertainty in relative pose measurements. Moreover it can be naturally extended to different communication technologies (bluetooth, RFId, wifi, etc.) regardless their sensing range. The proposed approach is validated through experimental test.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/23638302010-01-01T00:00:00ZApparato di illuminazione robotizzato e metodo di comandohttp://hdl.handle.net/11583/2287031Titolo: Apparato di illuminazione robotizzato e metodo di comando
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/11583/22870312009-01-01T00:00:00ZMulti Agent Localization from Noisy Relative Pose Measurementshttp://hdl.handle.net/11583/2422942Titolo: Multi Agent Localization from Noisy Relative Pose Measurements
Abstract: In this paper we address the problem of estimating the poses of a team of agents when they do not share any common reference frame. Each agent is capable of measuring the relative position and orientation of its neighboring agents, however these measurements are not exact but they are corrupted with noises. The goal is to compute the pose of each agent relative to an anchor node. We present a strategy where, first of all, the agents compute their orientations relative to the anchor. After that, they update the relative position measurements according to these orientations, to finally compute their positions. As contribution we discuss the proposed strategy, that has the interesting property that can be executed in a distributed fashion. The distributed implementation allows each agent to recover its pose using exclusively local information and local interactions with its neighbors. This algorithm has a low memory load, since it only requires each node to maintain an estimate of its own orientation and position.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/11583/24229422011-01-01T00:00:00ZNonlinear estimation techniques for autonomous navigation in single and multi robot systemshttp://hdl.handle.net/11583/2502532Titolo: Nonlinear estimation techniques for autonomous navigation in single and multi robot systems
Abstract: In this work we address different issues arising in mobile robot autonomous navigation. We mainly deal with localization and mapping problems in single and multi robot systems, although some contributions may escape from this classification, involving also decisional processes or more general problems and algorithms.
In the first part of the thesis we discuss several approaches for mobile robots
Simultaneous Localization and Mapping (SLAM): this problem arises when a mobile robot is deployed in an unknown environment and has to build a model of
the environment (map) while estimating its own position and orientation (robot
pose). SLAM is essentially a large nonlinear estimation problem, and, in this thesis, we address it using different estimation tools (graph-based approaches, Extended Kalman filter, and particle filters). We start by providing innovative insights on the mathematical structure of graph-based maximum likelihood approaches (pose graph optimization problem). We take advantage from these insights to devise efficient estimation strategies that enhance convergence properties of pose graph optimization while reducing the computational effort. Moreover, we propose a formal convergence analysis that justifies empirical observations of related work and provides non-trivial results on the aspects influencing global convergence of graph-based estimation schema based on Gauss-Newton methods. As a second contribution we study an Extended Kalman filter-based SLAM approach, investigating its observability properties and discussing several applications. A third contribution of the first part of the thesis deals with particle filters-based techniques; we present a multi robot extension of the SLAM problem, which relaxes common assumptions of related work. Moreover, we take advantage from the study of SLAM with particle filters to investigate the problem of autonomous exploration under uncertainty, proposing an innovative approach for exploration. This technique is shown to overcome several limitations of state-of-the-art techniques, thus underlining intrinsic limitations of particle filters-based exploration approaches.
The second part of the thesis is more heterogeneous, although a main focus is on distributed algorithms for estimation and optimization in multi agent systems. In several application scenarios the input data (e.g., sensor measurements) for performing estimation or optimization are acquired by different nodes that can be geographically distant. In centralized algorithms, input data are gathered by a central computational unit which is in charge of solving the problem for the entire network. In distributed algorithms, instead, the computation is fractioned among the nodes in a network, which have to exploit local computation and communication, in order to reach consensus on a global solution of the problem (e.g., a single estimate for the variable that network’s sensors are measuring). The distributed setup allows spreading the computation burden and the memory allocation among several processors, reducing inter-nodal communication, and increasing the robustness of the systems with respect to failures of the central computation unit. A first contribution of the second part of the thesis regards a distributed gradient method for multi robot localization from relative distance measurements. The distributed gradient method is proved to converge to the same solution of its centralized counterpart, while providing the benefits of a fully decentralized scheme that can be implemented autonomously by the nodes in the network. Extensive numerical experiments highlight the advantages of using the proposed technique, in terms of convergence speed, computational cost, and communication burden. The algorithm is also suitable for the case in which no synchronization exists among the nodes and it is shown to be scalable in the network size, since it requires inexpensive computation and low memory storage. The last contribution of the thesis is focused on a distributed approach for convex optimization, based on a constraint consensus strategy. We propose an approach, named active constraints consensus, and we show that it is particularly suitable for a specific class of convex programs under uncertainty (random convex programs). In the thesis we prove that, under suitable assumptions, the active constraints consensus algorithm has several desirable properties, including finite-time convergence and limited communication requirements. We also discuss applications of the distributed algorithm, including distributed estimation and classification.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11583/25025322012-01-01T00:00:00Z