Approaches to Vision-Based Formation Control

Author(s)
Calise, Anthony J.
Sattigeri, Ramachandra J.
Watanabe, Yoko
Madyastha, Venkatesh
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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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.
Sponsor
This work was supported in part by AFOSR MURI #F49620-03-1- 0401: Active Vision Control Systems for Complex Adversarial 3-D Environments.
Date
2004-12
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Proceedings
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