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Cooperative control of mobile sensor platforms in dynamic environments

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dc.contributor.authorRagi, Shankarachary, author
dc.contributor.authorChong, Edwin K. P., advisor
dc.contributor.authorKrapf, Diego, committee member
dc.contributor.authorLuo, J. Rockey, committee member
dc.contributor.authorOprea, Iuliana, committee member
dc.date.accessioned2007-01-03T06:40:48Z
dc.date.available2007-01-03T06:40:48Z
dc.date.issued2014
dc.description.abstractWe develop guidance algorithms to control mobile sensor platforms, for both centralized and decentralized settings, in dynamic environments for various applications. More precisely, we develop control algorithms for the following mobile sensor platforms: unmanned aerial vehicles (UAVs) with on-board sensors for multitarget tracking, autonomous amphibious vehicles for flood-rescue operations, and directional sensors (e.g., surveillance cameras) for maximizing an information-gain-based objective function. The following is a brief description of each of the above-mentioned guidance control algorithms. We develop both centralized and decentralized control algorithms for UAVs based on the theories of partially observable Markov decision process (POMDP) and decentralized POMDP (Dec-POMDP) respectively. Both POMDPs and Dec-POMDPs are intractable to solve exactly; therefore we adopt an approximation method called nominal belief-state optimization (NBO) to solve (approximately) the control problems posed as a POMDP or a Dec-POMDP. We then address an amphibious vehicle guidance problem for a flood rescue application. Here, the goal is to control multiple autonomous amphibious vehicles while minimizing the average rescue time of multiple human targets stranded in a flood situation. We again pose this problem as a POMDP, and extend the above-mentioned NBO approximation method to solve the guidance problem. In the final phase, we study the problem of controlling multiple 2-D directional sensors while maximizing an objective function based on the information gain corresponding to multiple target locations. This problem is found to be a combinatorial optimization problem, so we develop heuristic methods to solve the problem approximately, and provide analytical results on performance guarantees. We then improve the performance of our heuristics by applying an approximate dynamic programming approach called rollout.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierRagi_colostate_0053A_12245.pdf
dc.identifier.urihttp://hdl.handle.net/10217/82529
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectsensor fusion
dc.subjectpath planning for autonomous vehicles
dc.subjecttarget tracking
dc.subjectapplications of POMDP and Dec-POMDP
dc.subjectdecision making under uncertainty
dc.titleCooperative control of mobile sensor platforms in dynamic environments
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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