Generalized Optimal Control for Autonomous Mine Countermeasures Missions
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Authors
Kragelund, Sean
Walton, Claire
Kaminer, Isaac
Dobrokhodov, Vladimir
Subjects
Planning
Sonar detection
Trajectory
Autonomous vehicles
Optimal control
Computational modeling
Autonomous underwater vehicle (AUV)
autonomous vehicles
mine countermeasures (MCM)
motion planning
optimal control
unmanned surface vessel (USV)
unmanned vehicles
Sonar detection
Trajectory
Autonomous vehicles
Optimal control
Computational modeling
Autonomous underwater vehicle (AUV)
autonomous vehicles
mine countermeasures (MCM)
motion planning
optimal control
unmanned surface vessel (USV)
unmanned vehicles
Advisors
Date of Issue
2020
Date
Publisher
IEEE
Language
Abstract
This article presents a computational framework for planning mine countermeasures (MCM) search missions by autonomous vehicles. It employs generalized optimal control (GenOC), a model-based trajectory optimization approach, to maximize the expected search performance of vehicle–sensor pairs in different minehunting scenarios. We describe each element of the proposed framework and adapt it to solve real-world MCM motion planning problems. A key contribution of this article develops sensor models that are more tunable than conventional ones based on lateral range curves. The proposed models incorporate engineering parameters and 3-D geometry to compute mine detection probability as a function of sonar design and search vehicle trajectories. Specific examples for various forward-looking and sidescan sonar systems deployed by unmanned vehicles are included. Objective computations utilize these sonar detection models during optimization to minimize the risk that candidate search trajectories fail to detect mines in an area of interest. Simulation results highlight the flexibility of our proposed GenOC framework and confirm that optimal trajectories outperform conventional search patterns under time or resource constraints. We conclude by identifying some of the practical considerations of this approach, and suggest ways that numerical analysis of GenOC solutions can be used for MCM mission planning and decision aid development.
Type
Article
Preprint
Preprint
Description
The article of record as published may be found at https://doi.org/10.1109/JOE.2020.2998930
Series/Report No
Department
Mechanical and Aerospace Engineering (MAE)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
31 p.
Citation
Kragelund, Sean, et al. "Generalized Optimal Control for Autonomous Mine Countermeasures Missions." IEEE Journal of Oceanic Engineering (2020).
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.