Planning and Implementing Trajectories for Autonomous Underwater Vehicles to Track Evolving Ocean Processes Based on Predictions from a Regional Ocean Model

Authors:
Ryan N. Smith; Yi Chao;Peggy P. Li;David A. Caron;Burton H. Jones;Gaurav S. Sukhatme
Affiliations:
Robotic Embedded Systems Laboratory, University of SouthernCalifornia, Los Angeles, CA 90089, USA;Jet Propulsion Laboratory, California Institute of Technology,4800 Oak Grove Drive, Pasadena, CA 91109, USA;Jet Propulsion Laboratory, California Institute of Technology,4800 Oak Grove Drive, Pasadena, CA 91109, USA;Department of Biological Sciences, University of SouthernCalifornia, Los Angeles, CA 90089, USA;Department of Biological Sciences, University of SouthernCalifornia, Los Angeles, CA 90089, USA;Robotic Embedded Systems Laboratory, University of SouthernCalifornia, Los Angeles, CA 90089, USA
Venue:
International Journal of Robotics Research
Year:
2010

Citing 2
Cited 3

Slocum Gliders: Robust and ready: Research Articles

Journal of Field Robotics - Special Issue on Underwater Robotics
Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Proceedings of the Fourth ACM International Workshop on UnderWater Networks

Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters

International Journal of Robotics Research
Analysis of adaptive sampling techniques for underwater vehicles

Autonomous Robots
Glider CT: reconstructing flow fields from predicted motion of underwater gliders

Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend preliminary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.