6,898 research outputs found
A decentralized motion coordination strategy for dynamic target tracking
This paper presents a decentralized motion planning
algorithm for the distributed sensing of a noisy dynamical
process by multiple cooperating mobile sensor agents. This
problem is motivated by localization and tracking tasks of
dynamic targets. Our gradient-descent method is based on a
cost function that measures the overall quality of sensing. We
also investigate the role of imperfect communication between
sensor agents in this framework, and examine the trade-offs in
performance between sensing and communication. Simulations
illustrate the basic characteristics of the algorithms
Adaptive sampling in autonomous marine sensor networks
Submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy at the Massachusetts Institute of Technology and the
Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control
in complex environments with multiple constraints, and an approach to cooperative
robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches.
The mobility of the sensor platforms is a key advantage of this strategy, allowing
dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time.
Experimental results are presented for a 2-D target tracking application in which fully
autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification
platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate.
In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the
behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based
control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research
assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network
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
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