Quantitative soft gripper design tools to advance unmanned underwater vehicle (UUV) autonomous intervention
Abstract
Unmanned underwater vehicles (UUVs) are an increasingly critical part of U.S. Navy operations. UUVs have convincingly demonstratedthe 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 are currently only possible with remotely operated vehicles (ROVs), which require the presence of surface support ships for direct human control.There is a need to speed up the deformable robotics design cycle by producing appropriate estimation methodswhich will remove the long design times from the process. The current state of the art in soft robotics manipulator design allows for the simulation of the behavior of regular geometries undergoing slow internal pressure changes. The simulations are themselves resource intensive to perform, and thus are poorly suited to complete high resolution exploration of a design space even for these relatively simple shapes. Rather, they are used as part of heuristic design/simulate/iterate cycle driven by human input, followed by printing of a sufficiently high performance candidate, as in Figure 2. The estimation techniques developed in this effort are intended to replace this heuristically driven design cycle with a one which can rapidly produce an optimal design across a range of design parameters. The exploration of the design space will consist of initial parameterization, automated selection of a sparse set of designs withtin that parameterization, and then simulation of that sparse set to generate a performance estimator function across the entire design space. The final steps in the process will be simulation of the optimal design identified by the estimator to verify the expected performance, followed by printing and testing in hardware.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2023
- Source ID
- N000142312190
Entities
People
- Stephen Licht
Organizations
- Office of Naval Research
- United States Navy
- University of Rhode Island