Real-time center of buoyancy identification for optimal hovering in autonomous underwater intervention

This work addresses the problem of optimal positioning for an intervention AUV, minimizing the energy consumption and improving the stability in orientation. During a generic intervention task, the vehicle is generally maintained in a hovering configuration, thus requiring a 6 DOF control of the vehicle positioning. The choice of roll and pitch, if done arbitrarily, can severely impact the power efficiency of the vehicle, especially in heavy systems, since the center of buoyancy (COB) may not be necessarily aligned over the center of mass (COM). This approach uses an Extended Kalman Filter (EKF) to identify the location of the center of buoyancy relative to the center of mass, thus allowing to compute the working orientation that maintains the COB vertically aligned above the COM. The EKF is implemented online and hence is able to detect movements of the COB due for example to ballast operations. This algorithm has been firstly implemented in simulation and then successfully validated with the SAUVIM (Semi-Autonomous Underwater Vehicle for Intervention Missions) autonomous underwater vehicle. With its weight of about 4 tons, this testbed is an optimal platform for validating the precision of the filter, since a very small variation of the target pitch and roll results in a large restoring torque.

[1]  Thor I. Fossen,et al.  Singularity-free tracking of unmanned underwater vehicles in 6 DOF , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[2]  Thor I. Fossen,et al.  Position and attitude tracking of AUV's: a quaternion feedback approach , 1994 .

[3]  Junku Yuh,et al.  Underwater autonomous manipulation for intervention missions AUVs , 2009 .

[4]  Junku Yuh,et al.  Experimental study on adaptive control of underwater robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[5]  Gianluca Antonelli,et al.  A novel adaptive control law for autonomous underwater vehicles , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[6]  Gilmer L. Blankenship,et al.  Symbolic construction of models for multibody dynamics , 1995, IEEE Trans. Robotics Autom..

[7]  Gianluca Antonelli.  Underwater Robots , 2003 .

[8]  Song K. Choi,et al.  Experimental validation of model-based thruster fault detection for underwater vehicles , 2009, 2009 IEEE International Conference on Robotics and Automation.

[9]  Jens G. Balchen,et al.  The Nerov Autonomous Underwater Vehicle , 1991, OCEANS 91 Proceedings.

[10]  Chien Chern Cheah,et al.  Adaptive setpoint control for autonomous underwater vehicles , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[11]  J. Yuh,et al.  Design of a semi-autonomous underwater vehicle for intervention missions (SAUVIM) , 1998, Proceedings of 1998 International Symposium on Underwater Technology.