Information-theoretic coordinated control of multiple sensor platforms

Grocholsky, Ben; Makarenko, Alexei; Durrant‐Whyte, Hugh

doi:10.1109/robot.2003.1241807

Cited by 198 publications

(246 citation statements)

References 11 publications

Supporting

Mentioning

242

Contrasting

Unclassified

Order By: Relevance

“…A performance metric that accounts for both spatial and temporal sampling requirements was derived by Wilcox et al [48]; it was used to evaluate oceanographic survey performance with AUVs in [49]. A methodology for control of multiple sensor platforms based on information theory was presented in [50] and sampling strategies driven by distributed parameter estimation were described in [51,52].…”

Section: Background and Historymentioning

confidence: 99%

“…These ideas were extended further by Zhang and Leonard [65,66] to design provable control laws for cooperative level set tracking, whereby small vehicle groups could cooperate to generate contour plots of noisy, unknown fields, adjusting their formation shape to provide optimal filtering of their noisy measurements. Related work addressed environmental boundary tracking [67,68], coverage control [69,70], target tracking [71,72], and maximization of information [50].…”

Section: Background and Historymentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

View full text Add to dashboard Cite

Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

show abstract

Section: Background and Historymentioning

confidence: 99%

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

View full text Add to dashboard Cite

show abstract

“…A predicted gain is calculated as the amount of information the observation provides to the information matrix of the whole system. This is obtained by computing the Observation mutual information (OMI) [49], which is a measure of the amount of information with which one observation can provide the whole topology within a KF estimator. A new derivation that allows the OMI to be computed in an efficient way is also presented.…”

Section: Kalman Filter Based Image Mosaicing Approachesmentioning

confidence: 99%

Efficient Topology Estimation for Large Scale Optical Mapping

Elibol¹,

Gracias²,

García³

2013

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author. PhD AbstractLarge scale image mosaicing methods are in great demand among scientists who study different aspects of the seabed, and have been fostered by impressive advances in the capabilities of underwater robots in gathering optical data from the seafloor. Cost and weight constraints mean that low-cost Remotely operated vehicles (ROVs) usually have a very limited number of sensors. When a low-cost robot carries out a seafloor survey using a downlooking camera, it usually follows a predefined trajectory that provides several non time-consecutive overlapping image pairs. Finding these pairs (a process known as topology estimation) is indispensable to obtaining globally consistent mosaics and accurate trajectory estimates, which are necessary for a global view of the surveyed area, especially when optical sensors are the only data source.This thesis presents a set of consistent methods a...

show abstract

“…However, in the case of a real-time system working outdoors, a decentralized approach is more suitable due to its scalability and reliability (Makarenko et al, 2004;Grocholsky et al, 2003;Grocholsky, 2002). Bandwidth requirements are alleviated with a decentralized filter where each node only employs local information (data only from local sensors, for instance, the sensors on-board the robot), and then shares its estimation with other nodes.…”

Section: Perception Systemmentioning

confidence: 99%

A distributed architecture for a robotic platform with aerial sensor transportation and self‐deployment capabilities

Maza

Caballero

Capitán

et al. 2011

Journal of Field Robotics

View full text Add to dashboard Cite

This paper presents the architecture developed in the framework of the AWARE project for the autonomous distributed cooperation between unmanned aerial vehicles (UAVs), wireless sensor/actuator networks and ground camera networks. One of the main goals was the demonstration of useful actuation capabilities involving multiple ground and aerial robots in the context of civil applications. A novel characteristic is the demonstration in field experiments of the transportation and deployment of the same load with single/multiple autonomous aerial vehicles.The architecture is endowed with different modules that solve the usual problems that arise during the execution of multi-purpose missions, such as task allocation, conflict resolution, task decomposition and sensor data fusion. The approach had to satisfy two main requirements: robustness for the operation in disaster management scenarios and easy integration of different autonomous vehicles. The former specification led to a distributed design and the latter was tackled by imposing several requirements on the execution capabilities of the vehicles to be integrated in the platform.

show abstract

Information-theoretic coordinated control of multiple sensor platforms

Cited by 198 publications

References 11 publications

Cooperative Vehicle Environmental Monitoring

Cooperative Vehicle Environmental Monitoring

Efficient Topology Estimation for Large Scale Optical Mapping

A distributed architecture for a robotic platform with aerial sensor transportation and self‐deployment capabilities

Contact Info

Product

Resources

About