Title:
Approaches to Vision-Based Formation Control

dc.contributor.author Johnson, Eric N.
dc.contributor.author Calise, Anthony J.
dc.contributor.author Sattigeri, Ramachandra J.
dc.contributor.author Watanabe, Yoko
dc.contributor.author Madyastha, Venkatesh
dc.contributor.corporatename Georgia Institute of Technology. School of Aerospace Engineering
dc.date.accessioned 2010-11-09T20:04:06Z
dc.date.available 2010-11-09T20:04:06Z
dc.date.issued 2004-12
dc.description Presented at the 43rd IEEE Conference on Decision and Control; December 14-17, 2004; Atlantis, Paradise Island, Bahamas. en_US
dc.description ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.description.abstract This paper implements several methods for performing vision-based formation flight control of multiple aircraft in the presence of obstacles. No information is communicated between aircraft, and only passive 2-D vision information is available to maintain formation. The methods for formation control rely either on estimating the range from 2-D vision information by using Extended Kalman Filters or directly regulating the size of the image subtended by a leader aircraft on the image plane. When the image size is not a reliable measurement, especially at large ranges, we consider the use of bearing-only information. In this case, observability with respect to the relative distance between vehicles is accomplished by the design of a time-dependent formation geometry. To improve the robustness of the estimation process with respect to unknown leader aircraft acceleration, we augment the EKF with an adaptive neural network. 2-D and 3-D simulation results are presented that illustrate the various approaches. en_US
dc.description.sponsorship This work was supported in part by AFOSR MURI #F49620-03-1- 0401: Active Vision Control Systems for Complex Adversarial 3-D Environments. en_US
dc.identifier.citation Approaches to Vision-Based Formation Control. Eric N. Johnson, Anthony J. Calise, Ramachandra J. Sattigeri, Yoko Watanabe, Venkatesh Madyastha. Invited Session Paper. IEEE Conference on Decision and Control, December, 2004. en_US
dc.identifier.uri http://hdl.handle.net/1853/35875
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original IEEE
dc.subject Adaptive estimation en_US
dc.subject Estimation en_US
dc.subject Formation flight en_US
dc.subject Guidance en_US
dc.subject Obstacle avoidance en_US
dc.subject Vision en_US
dc.title Approaches to Vision-Based Formation Control en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Johnson, Eric N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Design Group
local.contributor.corporatename College of Engineering
local.contributor.corporatename Unmanned Aerial Vehicle Research Facility
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