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Anytime Planning for Decentralized Multirobot Active Information Gathering
Resilient Active Target Tracking with Multiple Robots
The problem of target tracking with multiple robots consists of actively
planning the motion of the robots to track the targets. A major challenge for
practical deployments is to make the robots resilient to failures. In
particular, robots may be attacked in adversarial scenarios, or their sensors
may fail or get occluded. In this paper, we introduce planning algorithms for
multi-target tracking that are resilient to such failures. In general,
resilient target tracking is computationally hard. Contrary to the case where
there are no failures, no scalable approximation algorithms are known for
resilient target tracking when the targets are indistinguishable, or unknown in
number, or with unknown motion model. In this paper we provide the first such
algorithm, that also has the following properties: First, it achieves maximal
resiliency, since the algorithm is valid for any number of failures. Second, it
is scalable, as our algorithm terminates with the same running time as
state-of-the-art algorithms for (non-resilient) target tracking. Third, it
provides provable approximation bounds on the tracking performance, since our
algorithm guarantees a solution that is guaranteed to be close to the optimal.
We quantify our algorithm's approximation performance using a novel notion of
curvature for monotone set functions subject to matroid constraints. Finally,
we demonstrate the efficacy of our algorithm through MATLAB and Gazebo
simulations, and a sensitivity analysis; we focus on scenarios that involve a
known number of distinguishable targets
Path Planning in Dynamic Environments with Adaptive Dimensionality
Path planning in the presence of dynamic obstacles is a challenging problem
due to the added time dimension in search space. In approaches that ignore the
time dimension and treat dynamic obstacles as static, frequent re-planning is
unavoidable as the obstacles move, and their solutions are generally
sub-optimal and can be incomplete. To achieve both optimality and completeness,
it is necessary to consider the time dimension during planning. The notion of
adaptive dimensionality has been successfully used in high-dimensional motion
planning such as manipulation of robot arms, but has not been used in the
context of path planning in dynamic environments. In this paper, we apply the
idea of adaptive dimensionality to speed up path planning in dynamic
environments for a robot with no assumptions on its dynamic model.
Specifically, our approach considers the time dimension only in those regions
of the environment where a potential collision may occur, and plans in a
low-dimensional state-space elsewhere. We show that our approach is complete
and is guaranteed to find a solution, if one exists, within a cost
sub-optimality bound. We experimentally validate our method on the problem of
3D vehicle navigation (x, y, heading) in dynamic environments. Our results show
that the presented approach achieves substantial speedups in planning time over
4D heuristic-based A*, especially when the resulting plan deviates
significantly from the one suggested by the heuristic.Comment: Accepted in SoCS 201
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Generic Multiview Visual Tracking
Recent progresses in visual tracking have greatly improved the tracking
performance. However, challenges such as occlusion and view change remain
obstacles in real world deployment. A natural solution to these challenges is
to use multiple cameras with multiview inputs, though existing systems are
mostly limited to specific targets (e.g. human), static cameras, and/or camera
calibration. To break through these limitations, we propose a generic multiview
tracking (GMT) framework that allows camera movement, while requiring neither
specific object model nor camera calibration. A key innovation in our framework
is a cross-camera trajectory prediction network (TPN), which implicitly and
dynamically encodes camera geometric relations, and hence addresses missing
target issues such as occlusion. Moreover, during tracking, we assemble
information across different cameras to dynamically update a novel
collaborative correlation filter (CCF), which is shared among cameras to
achieve robustness against view change. The two components are integrated into
a correlation filter tracking framework, where the features are trained offline
using existing single view tracking datasets. For evaluation, we first
contribute a new generic multiview tracking dataset (GMTD) with careful
annotations, and then run experiments on GMTD and the PETS2009 datasets. On
both datasets, the proposed GMT algorithm shows clear advantages over
state-of-the-art ones
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
We propose an online visual tracking algorithm by learning discriminative
saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained
on a large-scale image repository in offline, our algorithm takes outputs from
hidden layers of the network as feature descriptors since they show excellent
representation performance in various general visual recognition problems. The
features are used to learn discriminative target appearance models using an
online Support Vector Machine (SVM). In addition, we construct target-specific
saliency map by backpropagating CNN features with guidance of the SVM, and
obtain the final tracking result in each frame based on the appearance model
generatively constructed with the saliency map. Since the saliency map
visualizes spatial configuration of target effectively, it improves target
localization accuracy and enable us to achieve pixel-level target segmentation.
We verify the effectiveness of our tracking algorithm through extensive
experiment on a challenging benchmark, where our method illustrates outstanding
performance compared to the state-of-the-art tracking algorithms
Real-Time Area Coverage and Target Localization using Receding-Horizon Ergodic Exploration
Although a number of solutions exist for the problems of coverage, search and
target localization---commonly addressed separately---whether there exists a
unified strategy that addresses these objectives in a coherent manner without
being application-specific remains a largely open research question. In this
paper, we develop a receding-horizon ergodic control approach, based on hybrid
systems theory, that has the potential to fill this gap. The nonlinear model
predictive control algorithm plans real-time motions that optimally improve
ergodicity with respect to a distribution defined by the expected information
density across the sensing domain. We establish a theoretical framework for
global stability guarantees with respect to a distribution. Moreover, the
approach is distributable across multiple agents, so that each agent can
independently compute its own control while sharing statistics of its coverage
across a communication network. We demonstrate the method in both simulation
and in experiment in the context of target localization, illustrating that the
algorithm is independent of the number of targets being tracked and can be run
in real-time on computationally limited hardware platforms.Comment: 18 page
FLORIS and CLORIS: Hybrid Source and Network Localization Based on Ranges and Video
We propose hybrid methods for localization in wireless sensor networks fusing
noisy range measurements with angular information (extracted from video).
Compared with conventional methods that rely on a single sensed variable, this
may pave the way for improved localization accuracy and robustness. We address
both the single-source and network (i.e., cooperative multiple-source)
localization paradigms, solving them via optimization of a convex surrogate.
The formulations for hybrid localization are unified in the sense that we
propose a single nonlinear least-squares cost function, fusing both angular and
range measurements. We then relax the problem to obtain an estimate of the
optimal positions. This contrasts with other hybrid approaches that alternate
the execution of localization algorithms for each type of measurement
separately, to progressively refine the position estimates. Single-source
localization uses a semidefinite relaxation to obtain a one-shot matrix
solution from which the source position is derived via factorization. Network
localization uses a different approach where sensor coordinates are retained as
optimization variables, and the relaxed cost function is efficiently minimized
using fast iterations based on Nesterov's optimal method. Further, an automated
calibration procedure is developed to express range and angular information,
obtained by different devices, possibly deployed at different locations, in a
single consistent coordinate system. This drastically reduces the need for
manual calibration that would otherwise negatively impact the practical
usability of hybrid range/video localization systems. We develop and test, both
in simulation and experimentally, the new hybrid localization algorithms, which
not only overcome the limitations of previous fusing approaches but also
compare favorably to state-of-the-art methods, outperforming them in some
scenarios
A multi-projector CAVE system with commodity hardware and gesture-based interaction
Spatially-immersive systems such as CAVEs provide users with surrounding worlds by projecting 3D models on multiple screens around the viewer. Compared to alternative immersive systems such as HMDs, CAVE systems are a powerful tool for collaborative inspection of virtual environments due to better use of peripheral vision, less sensitivity to tracking errors, and higher communication possibilities among users. Unfortunately, traditional CAVE setups require sophisticated equipment including stereo-ready projectors and tracking systems with high acquisition and maintenance costs. In this paper we present the design and construction of a passive-stereo, four-wall CAVE system based on commodity hardware. Our system works with any mix of a wide range of projector models that can be replaced independently at any time, and achieves high resolution and brightness at a minimum cost. The key ingredients of our CAVE are a self-calibration approach that guarantees continuity across the screen, as well as a gesture-based interaction approach based on a clever
combination of skeletal data from multiple Kinect sensors.Preprin
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