9,073 research outputs found
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Optimal circular flight of multiple UAVs for target tracking in urban areas
This work is an extension of our previous result in which a novel single-target tracking
algorithm for fixed-wing UAVs (Unmanned Air Vehicles) was proposed. Our previous
algorithm firstly finds the centre of a circular flight path, rc, over the interested ground
target which maximises the total chance of keeping the target inside the camera field of view
of UAVs, , while the UAVs fly along the circular path. All the UAVs keep their maximum
allowed altitude and fly along the same circle centred at rc with the possible minimum turn
radius of UAVs. As discussed in [1,4], these circular flights are highly recommended for
various target tracking applications especially in urban areas, as for each UAV the
maximum altitude flight ensures the maximum visibility and the minimum radius turn
keeps the minimum distance to the target at the maximum altitude.
Assuming a known probability distribution for the target location, one can quantify ,
which is incurred by the travel of a single UAV along an arbitrary circle, using line-of-sight
vectors. From this observation, (the centre of) an optimal circle among numerous feasible
ones can be obtained by a gradient-based search combined with random sampling, as
suggested in [1]. This optimal circle is then used by the other UAVs jointly tracking the
same target. As the introduction of multiple UAVs may minimise further, the optimal
spacing between the UAVs can be naturally considered. In [1], a typical line search method
is suggested for this optimal spacing problem. However, as one can easily expect, the
computational complexity of this search method may undesirably increase as the number of
UAVs increases.
The present work suggests a remedy for this seemingly complex optimal spacing problem.
Instead of depending on time-consuming search techniques, we develop the following
algorithm, which is computationally much more efficient. Firstly, We calculate the
distribution (x), where x is an element of , which is the chance of capturing the target by
one camera along . Secondly, based on the distribution function, (x), find separation
angles between UAVs such that the target can be always tracked by at least one UAV with a
guaranteed probabilistic measure. Here, the guaranteed probabilistic measure is chosen by
taking into account practical constraints, e.g. required tracking accuracy and UAVs'
minimum and maximum speeds. Our proposed spacing scheme and its guaranteed
performance are demonstrated via numerical simulations
Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications
This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.Comisión Europea H2020 644271Comisión Europea FP7 288082Ministerio de Economia, Industria y Competitividad DPI2015-71524-RMinisterio de Economia, Industria y Competitividad DPI2014-5983-C2-1-RMinisterio de Educación, Cultura y Deporte FP
Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem
Aimed at solving the optimal control problem for nonlinear systems with
unsymmetrical input constraints, we present an online adaptive approach for
partially unknown control systems/dynamics. The designed algorithm converges
online to the optimal control solution without the knowledge of the internal
system dynamics. The optimality of the obtained control policy and the
stability for the closed-loop dynamic optimality are proved theoretically. The
proposed method greatly relaxes the assumption on the form of the internal
dynamics and input constraints in previous works. Besides, the control design
framework proposed in this paper offers a new approach to solve the optimal
circumnavigation problem involving a moving target for a fixed-wing unmanned
aerial vehicle (UAV). The control performance of our method is compared with
that of the existing circumnavigation control law in a numerical simulation and
the simulation results validate the effectiveness of our algorithm
Detecting Invasive Insects with Unmanned Aerial Vehicles
A key aspect to controlling and reducing the effects invasive insect species
have on agriculture is to obtain knowledge about the migration patterns of
these species. Current state-of-the-art methods of studying these migration
patterns involve a mark-release-recapture technique, in which insects are
released after being marked and researchers attempt to recapture them later.
However, this approach involves a human researcher manually searching for these
insects in large fields and results in very low recapture rates. In this paper,
we propose an automated system for detecting released insects using an unmanned
aerial vehicle. This system utilizes ultraviolet lighting technology, digital
cameras, and lightweight computer vision algorithms to more quickly and
accurately detect insects compared to the current state of the art. The
efficiency and accuracy that this system provides will allow for a more
comprehensive understanding of invasive insect species migration patterns. Our
experimental results demonstrate that our system can detect real target insects
in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page
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