Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields

Baronov, Dimitar; Baillieul, John

doi:10.1109/jproc.2011.2174101

Cited by 7 publications

(14 citation statements)

References 30 publications

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“…The aim is to study the search styles of the players in acquiring information about both the topology and the geometry of potential functions. Early results are reported in [18] …”

Section: Discussionmentioning

confidence: 99%

“…See [18] for detailed proofs of these propositions. The conditional complexity allows us to describe the evolution of the reconnaissance as iterative information acquisition.…”

Section: A the Conditional Complexitymentioning

confidence: 99%

“…For conditions under which a sample function of a random field is a Morse function, we refer the reader to the references in [18], and in particular to the important volume [19].…”

Section: Topology Of a Scalar Potential Fieldmentioning

confidence: 99%

“…MONTE CARLO VALIDATION The functions used for the Monte Carlo validation of the proposed reconnaissance protocols are sums of a random number, n ∈ {5, · · · , 10}, of Gaussian densities having equal means and variance and being uniformly distributed in the search domain. A similar set of problems in which the functions, f , are sampled from Gaussian random fields is treated in [18].…”

Section: B Open Loop Scan Strategymentioning

confidence: 99%

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Topology guided search of potential fields

Baronov

Baillieul

2010

49th IEEE Conference on Decision and Control (CDC)

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Using reactive control algorithms and a dictionary of motion primitives, we have developed search and reconnaissance protocols for mobile robots aimed at efficiently discovering key features of an unknown potential field's topology (as opposed to its geometry). This work has in a natural way led to the creation of parsimonious reconnaissance routines that do not rely on any prior knowledge about the environment. The design of topology guided search protocols uses a mathematical framework that quantifies the relationship between what is discovered and what remains to be discovered. This quantification rests on an information theory inspired model, whose properties allow us to treat search as a problem in optimal information acquisition. The main idea is that "conservative" and "aggressive" search strategies can be precisely defined, and search decisions regarding exploration vs. exploitation choices can be informed by our information-like metric.

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“…The aim is to study the search styles of the players in acquiring information about both the topology and the geometry of potential functions. Early results are reported in [18] …”

Section: Discussionmentioning

confidence: 99%

“…See [18] for detailed proofs of these propositions. The conditional complexity allows us to describe the evolution of the reconnaissance as iterative information acquisition.…”

Section: A the Conditional Complexitymentioning

confidence: 99%

“…For conditions under which a sample function of a random field is a Morse function, we refer the reader to the references in [18], and in particular to the important volume [19].…”

Section: Topology Of a Scalar Potential Fieldmentioning

confidence: 99%

Section: B Open Loop Scan Strategymentioning

confidence: 99%

See 2 more Smart Citations

Topology guided search of potential fields

Baronov

Baillieul

2010

49th IEEE Conference on Decision and Control (CDC)

Self Cite

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“…Importantly, humans are capable of intelligent and adaptable decision making in response to reasoning about realworld information in real-time. Progress has been reported on humans and robots in exploration [146,147,148,149,150] and how humans work together in search tasks [151,152,153,154]. However, more work is needed to leverage what cognitive scientists understand about human decision making and behavior.…”

Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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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...

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