Intelligent obstacle avoidance system for unmanned undersea vehicles in shallow water

Keehoon Kim; Andrew A. Kostrzewski; Daniel A. Erwin

doi:10.1117/12.543447

1 September 2004 Intelligent obstacle avoidance system for unmanned undersea vehicles in shallow water

Keehoon Kim, Andrew A. Kostrzewski, Daniel A. Erwin

Proceedings Volume 5417, Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI; (2004) https://doi.org/10.1117/12.543447
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

An unmanned undersea vehicle (UUV) needs an obstacle avoidance capability to make autonomous path planning decisions for successful undersea search and survey, maritime reconnaissance, communication/navigation aids, and tracking and trailing in uncharted shallow water. Physical Optics Corporation (POC) has developed a novel autonomous UUV path optimization navigator system for real-time, robust, self-adjusting, intelligent autonomous obstacle avoidance/navigation of UUVs. The POC system is based on our proprietary fast genetic algorithm, which processes signals from on-board obstacle avoidance sonar sensors to continuously optimize the navigation path while avoiding both moving and stationary obstacles in shallow waters. The system performs autonomous obstacle avoidance, accommodating navigation parameter changes. Vehicle dynamics are also incorporated by hydrodynamic compensation.

Citation Download Citation

Keehoon Kim, Andrew A. Kostrzewski, and Daniel A. Erwin "Intelligent obstacle avoidance system for unmanned undersea vehicles in shallow water", Proc. SPIE 5417, Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI, (1 September 2004); https://doi.org/10.1117/12.543447

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available