The University of Southern Mississippi builds and uses simulation apps to integrate machine learning models into the training of uncrewed underwater and surface vehicles.
On a calm day along the coast of Mississippi, a small surface vessel is pulled methodically back and forth in a test area. This low magnetic signature sensor platform is hard at work scanning the seabed for ferromagnetic targets. These scans are just one part of an ambitious project conducted by the University of Southern Mississippi’s (USM) Roger F. Wicker Center for Ocean Enterprise. Through a combination of modelling, simulation and machine learning (ML), USM researchers are laying the groundwork for intelligent uncrewed underwater vehicles (UUVs) capable of autonomously and accurately revealing what lies beneath the waves.
Modelling Ferromagnetic Structures on the Ocean Floor
Resting on the bottom of most harbours (and the ocean floor more generally) are ferromagnetic materials such as anthropogenic structures and debris, and even unexploded ordnance. To keep harbour traffic from hitting these hazards, navigational charts need to accurately reflect their locations. One of the more effective methods for scanning for these underwater magnetic anomalies is by using UUVs with advanced sensor equipment. However, these vehicles come with certain challenges, as they are expensive to build, their physical scans are complex, and, due to significant platform noise and environmental noise (such as environmental clutter and wind and waves), they do not always paint an accurate picture. To further complicate matters, the nonuniqueness of certain seafloor signatures can cause scans of different subsurface configurations to look similar. A team at USM is researching how to overcome these challenges and pave the way for better UUV-based sensing and mapping.
Continue Reading the Full User Story Here
About COMSOL
The COMSOL Multiphysics® modelling and simulation software is used in all fields of engineering, manufacturing and scientific research to simulate designs, devices and processes. The platform is an integrated environment for creating physics-based models and simulation applications.
Apprenticeship Funding Cuts to Cost Employers £214m
I had no idea the taxpayer was funding such an advanced level of education. Certainly graduates and post-graduates don´t receive comparable financial...