Title:
Approaches to Vision-Based Formation Control
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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