Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

Autonomous Underwater Vehicles (AUVs) operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability. Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities. To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment. Unlike previous works, the more realistic and applied case of constant thrust power navigation is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable. Conversely, the straight line path between starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent of the spatial structure of the eddy field.

[1]  A. Caiti,et al.  A Genetic Algorithm for Autonomous Undetwater Vehicle Route Planning in Ocean Environments with Complex Space-Time Variability , 2001 .

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  Allan R. Robinson,et al.  Real-time regional forecasting , 1996 .

[4]  K. Ganesan,et al.  Case-based path planning for autonomous underwater vehicles , 1994, Auton. Robots.

[5]  Henrik Schmidt,et al.  Underwater Vehicle Networks for Acoustic and Oceanographic Measurements in the Littoral Ocean , 2000 .

[6]  C. Vasudevan,et al.  Case-based path planning for autonomous underwater vehicles , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[7]  Andrea Caiti,et al.  Interactions of autonomous underwater vehicles with variable scale ocean structures , 2002 .

[8]  E. L. Nelson,et al.  AUV path planning: an A* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[9]  P. Malanotte‐Rizzoli Modern approaches to data assimilation in ocean modeling , 1996 .

[10]  A. Caiti,et al.  Evolutionary path planning for autonomous underwater vehicles in a variable ocean , 2004, IEEE Journal of Oceanic Engineering.

[11]  James C. McWilliams,et al.  Computer Modeling in Physical Oceanography from the Global Circulation to Turbulence , 1987 .

[12]  C. W. Warren,et al.  A technique for autonomous underwater vehicle route planning , 1990, Symposium on Autonomous Underwater Vehicle Technology.

[13]  Junku Yuh,et al.  GA-Based Motion Planning For Underwater Robotic Vehicles , 1997 .