Abstract
We present an algorithm for estimating thruster configurations of underwater vehicles with 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Choi, H., Hanai, A., Choi, S., Yuh, J.: Development of an underwater robot, ODIN-III. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 1, pp. 836–841 (2003)
Doniec, M., Vasilescu, I., Detweiler, C., Rus, D.: Complete se(3) underwater robot control with arbitrary thruster configurations. In: Proc. of the International Conference on Robotics and Automation, Anchorage, Alaska (2010)
Dunbabin, M., Roberts, J., Usher, K., Winstanley, G., Corke, P.: A hybrid AUV design for shallow water reef navigation. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 2105–2110 (2005)
Farrell, J., Goldenthal, B., Govindarajan, K.: Connectionist learning control systems: submarine depth control. In: Proceedings of the 29th IEEE Conference on Decision and Control 1990, vol. 4, pp. 2362–2367 (1990), doi:10.1109/CDC.1990.204050
Gaskett, C., Wettergreen, D., Zelinsky, A.: Reinforcement learning applied to the control of an autonomous underwater vehicle. In: Proc. of the Australian Conference on Robotics and Automation (AUCRA 1999), pp. 125–131 (1999)
Guo, J., Chiu, F., Wang, C.C.: Adaptive control of an autonomous underwater vehicle testbed using neural networks. In: MTS/IEEE. Challenges of Our Changing Global Environment. Conference Proceedings, OCEANS 1995, vol. 2, pp. 1033–1039 (1995), doi:10.1109/OCEANS.1995.528563
Ishii, K., Ura, T., Fujii, T.: A feedforward neural network for identification and adaptive control of autonomous underwater vehicles. In: 1994 IEEE International Conference on Neural Networks, IEEE World Congress on Computational Intelligence, vol. 5, pp. 3216–3221 (1994), doi:10.1109/ICNN.1994.374750
Kodogiannis, V.S., Lisboa, P.J.G., Lucas, J.: Neural network modelling and control for underwater vehicles. Artificial Intelligence in Engineering 10(3), 203–212 (1996), doi:10.1016/0954-1810(95)00029-1
Lorentz, J., Yuh, J.: A survey and experimental study of neural network auv control. In: Proceedings of the 1996 Symposium on Autonomous Underwater Vehicle Technology, AUV 1996, pp. 109–116 (1996), doi:10.1109/AUV.1996.532406
Vaganay, J., Elkins, M., Esposito, D., O’Halloran, W., Hover, F., Kokko, M.: Ship hull inspection with the HAUV: US navy and NATO demonstrations results. In: OCEANS 2006, pp. 1–6 (2006)
Vasilescu, I., Detweiler, C., Doniec, M., Gurdan, D., Sosnowski, S., Stumpf, J., Rus, D.: Amour v: A hovering energy efficient underwater robot capable of dynamic payloads. International Journal of Robotics Research, IJRR (2010)
van de Ven, P., Flanagan, C., Toal, D.: Identification of underwater vehicle dynamics with neural networks. In: MTTS/IEEE TECHNO-OCEAN, OCEANS 2004, vol. 3, pp. 1198–1204 (2004), doi:10.1109/OCEANS.2004.1405750
van de Ven, P.W., Flanagan, C., Toal, D.: Neural network control of underwater vehicles. Engineering Applications of Artificial Intelligence 18(5), 533–547 (2005), doi:10.1016/j.engappai.2004.12.004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Doniec, M., Detweiler, C., Rus, D. (2014). Estimation of Thruster Configurations for Reconfigurable Modular Underwater Robots. In: Khatib, O., Kumar, V., Sukhatme, G. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28572-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-28572-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28571-4
Online ISBN: 978-3-642-28572-1
eBook Packages: EngineeringEngineering (R0)