1,721,108 research outputs found
Nonlinear estimation techniques for autonomous navigation in single and multi robot systems
No full textIn 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 classificatio
A comparative study on robust localization: fault tolerance and robustness test on probabilistic filters for range-based positioning
No full textPreliminary results on robust global localization: fault tolerance and robustness test on Probabilistic Shaping
No full textProbabilistic Shaping with radial gaussian: a probabilistic approach to 2D and 3D robotic multilateration
No full textA Distributed Technique for Localization of Agent Formations from Relative Range Measurements
Full text linkAutonomous 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 structur
D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration
Full text linkFor a multi-robot team that collaboratively explores an unknown environment,
it is of vital importance that collected information is efficiently shared
among robots in order to support exploration and navigation tasks. Practical
constraints of wireless channels, such as limited bandwidth, urge robots to
carefully select information to be transmitted. In this paper, we consider the
case where environmental information is modeled using a 3D Scene Graph, a
hierarchical map representation that describes both geometric and semantic
aspects of the environment. Then, we leverage graph-theoretic tools, namely
graph spanners, to design greedy algorithms that efficiently compress 3D Scene
Graphs with the aim of enabling communication between robots under bandwidth
constraints. Our compression algorithms are navigation-oriented in that they
are designed to approximately preserve shortest paths between locations of
interest, while meeting a user-specified communication budget constraint. The
effectiveness of the proposed algorithms is demonstrated in synthetic robot
navigation experiments in a realistic simulator. A video abstract is available
at https://youtu.be/nKYXU5VC6A8.Comment: 18 pages, 18 figures; accepted at IEEE RA-L 202
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Full text linkRecent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted at IEEE TNS
From Sensor to Processing Networks: Optimal Estimation with Computation and Communication Latency
Full text linkThis paper investigates the use of a networked system (e.g., swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of communication and computation latency. Recent advances in edge computing have enabled processing to be spread across the network, hence we investigate the fundamental communication-computation trade-off, arising when a sensor has to decide whether to transmit raw data (incurring communication delay) or preprocess them (incurring computational delay) in order to compute an accurate estimate of the state of the phenomenon of interest. We propose two key contributions. First, we formalize the notion of processing network. Contrarily to sensor and communication networks, where the designer is concerned with the design of a suitable communication policy, in a processing network one can also control when and where the computation occurs in the network. The second contribution is to provide analytical results on the optimal preprocessing delay (i.e., the optimal time spent on computations at each sensor) for the case with a single sensor and multiple homogeneous sensors. Numerical results substantiate our claims that accounting for computation latencies (both at sensor and estimator side) and communication delays can largely impact the estimation accuracy
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