Advances In Unmanned Underwater Vehicle (UUV) Autonomouse Intervention

Abstract

Unmanned underwater vehicles (UUVs) are an increasingly critical part of U.S. Navy operations. UUVs have convincingly demonstrated the ability to protect warfighters by covertly enhancing situational awareness. UUVs excel at the collection of oceanographic data,detection of sea mines and unexploded ordnance, hull inspection, and intelligence, surveillance and reconnaissance (ISR) for forward deployed units. However, physical intervention tasks such as robotic recovery, repositioning, and modification/repair of underseaequipment is currently only possible with remotely operated vehicles (ROVs), which require the presence of surface support ships for direct human control.Conventional deep-sea manipulators must be positioned with extremely high accuracy to function properly; current vehicle control approaches cannot operate at this level of accuracy except at cripplingly slow operational tempos. Our approachis to tackle this problem from both ends: to redesign the manipulators to reduce the accuracy required, while simultaneously improving dynamic modeling and control of the vehicle to meet the (newly relaxed) positioning requirements at a high tempo.The manipulatordesign effort is focused on hybrid soft robotic grippers. Soft, low pressure, fluid driven manipulators are more forgiving of errors in gripper placement and orientation. Soft grippers also reduce deployed payload size and energy use, both of which are fundamental limitations in small UUV design. The vehicle control and modeling effort is focused on the effective combination of multiple propulsion technologies to produce capabilities that are better than the sum of the parts. By developing mathematical models to represent the strengths and weakness of control fins, buoyancy drives, and cross-body thrusters, we can quantitatively fuse their contributions to meet accuracy and precision requirements while minimizing energy usage.

Document Details

Document Type
DoD Grant Award
Publication Date
Apr 06, 2021
Source ID
N000142112179

Entities

People

  • Stephen Licht

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Rhode Island

Tags

Readers

  • Robotics and Automation.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - UAVs
  • Space
  • Space - Spacecraft Maneuvers