Application of Feedforward Control to Pan-Tilt Cameras on Planetary Rovers
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Future rover missions will be enhanced through the addition of science to the planetary traverse phase. Scientific targets are selected through a random search and salient gradient tracking in the visual field, which requires both a search algorithm and a reactive pan-tilt camera controller. This thesis presents a cerebellar-like reactive pan-tilt controller to track salient targets in the visual field as the rover moves based off the cerebellar models and the human vestibulo-ocular reflex. An online neural network using an EKF training law is used as a feed forward controller and it's performance is compared to standard batch and online neural network training techniques. The controller was then applied to the Barrett WAM to control the manipulator wrist. The online EKF trained network is able to adequately model the internal dynamics of a pan-tilt, while remaining stable due to the continuous learning. This is shown in both simulation and practice.
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Copyright © 2014 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2014
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ross-applicationoffeedforwardcontroltopantiltcameras.pdf | 2023-05-04 | Public | Download |