Estimation of Thruster Configurations for Reconfigurable Modular Underwater Robots
Name
DoniecDR_ISER2010.pdf
Description
Accepted version
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916.3 KB
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Checksum (MD5)
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Author(s)
Doniec, Marek Wojciech
Detweiler, Carrick
Rus, Daniela L
Date Issued
2014
Publisher
Springer Berlin Heidelberg
Citation
Doniec, Marek, Detweiler, Carrick and Rus, Daniela. 2014. "Estimation of Thruster Configurations for Reconfigurable Modular Underwater Robots."
Version
Author's final manuscript
Abstract
© Springer-Verlag Berlin Heidelberg 2014. We present an algorithm for estimating thruster configurations of underwater vehicleswith reconfigurable thrusters. The algorithm estimates each thruster’s effect on the vehicle’s attitude and position. The estimated parameters are used to maintain the robot’s attitude and position. The algorithm operates by measuring impulse response of individual thrusters and thruster combinations. Statistical metrics are used to select data samples. Finally, we compute a Moore-Penrose pseudoinverse, which is used to project the desired attitude and position changes onto the thrusters. We verify our algorithm experimentally using our robot AMOUR. The robot consists of a main body with a variable number of thrusters that can be mounted at arbitrary locations. It utilizes an IMU and a pressure sensor to continuously compute its attitude and depth. We use the algorithm to estimate different thruster configurations and show that the estimated parameters successfully control the robot. The gathering of samples together with the estimation computation takes approximately 40 seconds. Further, we show that the performance of the estimated controller matches the performance of a manually tuned controller. We also demonstrate that the estimation algorithm can adapt the controller to unexpected changes in thruster positions. The estimated controller greatly improves the stability and maneuverability of the robot when compared to the manually tuned controller.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-642-28572-1_45
