2,434 research outputs found
Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.
This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture.
The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks.
In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd
Low cost underwater acoustic localization
Over the course of the last decade, the cost of marine robotic platforms has
significantly decreased. In part this has lowered the barriers to entry of
exploring and monitoring larger areas of the earth's oceans. However, these
advances have been mostly focused on autonomous surface vehicles (ASVs) or
shallow water autonomous underwater vehicles (AUVs). One of the main drivers
for high cost in the deep water domain is the challenge of localizing such
vehicles using acoustics. A low cost one-way travel time underwater ranging
system is proposed to assist in localizing deep water submersibles. The system
consists of location aware anchor buoys at the surface and underwater nodes.
This paper presents a comparison of methods together with details on the
physical implementation to allow its integration into a deep sea micro AUV
currently in development. Additional simulation results show error reductions
by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ
Certifiably Correct Range-Aided SLAM
We present the first algorithm to efficiently compute certifiably optimal
solutions to range-aided simultaneous localization and mapping (RA-SLAM)
problems. Robotic navigation systems increasingly incorporate point-to-point
ranging sensors, leading to state estimation problems in the form of RA-SLAM.
However, the RA-SLAM problem is significantly more difficult to solve than
traditional pose-graph SLAM: ranging sensor models introduce non-convexity and
single range measurements do not uniquely determine the transform between the
involved sensors. As a result, RA-SLAM inference is sensitive to initial
estimates yet lacks reliable initialization techniques. Our approach,
certifiably correct RA-SLAM (CORA), leverages a novel quadratically constrained
quadratic programming (QCQP) formulation of RA-SLAM to relax the RA-SLAM
problem to a semidefinite program (SDP). CORA solves the SDP efficiently using
the Riemannian Staircase methodology; the SDP solution provides both (i) a
lower bound on the RA-SLAM problem's optimal value, and (ii) an approximate
solution of the RA-SLAM problem, which can be subsequently refined using local
optimization. CORA applies to problems with arbitrary pose-pose, pose-landmark,
and ranging measurements and, due to using convex relaxation, is insensitive to
initialization. We evaluate CORA on several real-world problems. In contrast to
state-of-the-art approaches, CORA is able to obtain high-quality solutions on
all problems despite being initialized with random values. Additionally, we
study the tightness of the SDP relaxation with respect to important problem
parameters: the number of (i) robots, (ii) landmarks, and (iii) range
measurements. These experiments demonstrate that the SDP relaxation is often
tight and reveal relationships between graph rigidity and the tightness of the
SDP relaxation.Comment: 17 pages, 9 figures, submitted to T-R
SCORE: A Second-Order Conic Initialization for Range-Aided SLAM
We present a novel initialization technique for the range-aided simultaneous
localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements
of point-to-point distances in addition to measurements of rigid
transformations to landmark or pose variables. Standard formulations of RA-SLAM
approach the problem as non-convex optimization, which requires a good
initialization to obtain quality results. The initialization technique proposed
here relaxes the RA-SLAM problem to a convex problem which is then solved to
determine an initialization for the original, non-convex problem. The
relaxation is a second-order cone program (SOCP), which is derived from a
quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM
problem. As a SOCP, the method is highly scalable. We name this relaxation
Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work
represents the first convex relaxation for RA-SLAM. We present real-world and
simulated experiments which show SCORE initialization permits the efficient
recovery of quality solutions for a variety of challenging single- and
multi-robot RA-SLAM problems with thousands of poses and range measurements.Comment: 9 pages, 8 figures, extended version of paper submitted to ICRA 202
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Secure Vehicular Communication Systems: Implementation, Performance, and Research Challenges
Vehicular Communication (VC) systems are on the verge of practical
deployment. Nonetheless, their security and privacy protection is one of the
problems that have been addressed only recently. In order to show the
feasibility of secure VC, certain implementations are required. In [1] we
discuss the design of a VC security system that has emerged as a result of the
European SeVeCom project. In this second paper, we discuss various issues
related to the implementation and deployment aspects of secure VC systems.
Moreover, we provide an outlook on open security research issues that will
arise as VC systems develop from today's simple prototypes to full-fledged
systems
LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN
The localization of the sensor nodes is a fundamental problem in wireless sensor networks.
There are a lot of different kinds of solutions in the literature. Some of them use external
devices like GPS, while others use special hardware or implicit parameters in wireless
communications.
In applications like wildlife localization in a natural environment, where the power available
and the weight are big restrictions, the use of hungry energy devices like GPS or hardware
that add extra weight like mobile directional antenna is not a good solution.
Due to these reasons it would be better to use the localization’s implicit characteristics in
communications, such as connectivity, number of hops or RSSI. The measurement related
to these parameters are currently integrated in most radio devices. These measurement
techniques are based on the beacons’ transmissions between the devices.
In the current study, a novel tracking distributed method, called LIS, for localization of
the sensor nodes using moving devices in a network of static nodes, which have no additional
hardware requirements is proposed.
The position is obtained with the combination of two algorithms; one based on a local
node using a fuzzy system to obtain a partial solution and the other based on a centralized
method which merges all the partial solutions. The centralized algorithm is based on the
calculation of the centroid of the partial solutions.
Advantages of using fuzzy system versus the classical Centroid Localization (CL)
algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for
testing localization algorithms.
With this simulator, it is demonstrated that the proposed method obtains less localization
errors and better accuracy than the centroid algorithm.Junta de AndalucĂa P07-TIC-0247
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