Brief

Researchers have developed a novel approach to 3D underwater path planning by using generative flow field surrogates, specifically conditional generative adversarial networks (cGANs). These cGANs, including a PatchGAN and a 2D3DGAN with self-attention, can replace computationally expensive Reynolds-Averaged Navier-Stokes (RANS) simulations. The generative models synthesize complex flow field volumes rapidly, enabling real-time path planning for autonomous underwater vehicles (AUVs). This method significantly reduces energy expenditure and avoids high-velocity wake encounters, offering a practical solution for maritime robotics. AI

DARPA has initiated the Deep Thoughts program to develop compact, cost-effective autonomous undersea vehicles (AUVs) capable of reaching full-ocean depths. The initiative aims to significantly reduce the size, cost, and development time of these deep-ocean systems by leveraging advancements in materials, manufacturing, and design. The program seeks novel approaches to subsystem architecture and manufacturing techniques to enable rapid design and integration, with a stated goal of providing a strategic advantage through responsive and scalable deep-ocean access. AI