Case Study: Space-Industry Robotics Technology Leveraged to Realize Significant Improvements in Offshore Safety and Sustainability

Abstract Historically, subsea vehicles were controlled by communications through a tether, which presents several challenges including a limited range of motion, deployment and retrieval, cost and logistics, and environmental impact. These challenges inspired the development of a new generation of robotic solutions seeking to transform the industry into an economically efficient and environmentally sustainable model. Autonomous systems are increasingly providing economic and environmental value, such as intelligent robots with subsea applications in energy, defense, transportation, and aquaculture. Combined with AI capabilities, these robots can make subsea work more sustainable. The paper presents findings from a robotics company established in 2014 in Texas by NASA-bred entrepreneurs. Today, the company exports its robotic services and vehicles internationally. Findings offer a first-hand insight into the development of the interconnected, purpose-built ecosystem of surface and subsea robots. The technology leverages autonomous behaviors, acoustic communications, over-the-horizon networking, force/torque controllable electric manipulation, AI and machine learning, and multimodal 3D workspace sensors. It is inspired by NASA's command and control approach for robots in space. When this framework is applied to subsea systems, it removes the need for the tether through the autonomous capabilities onboard the robotic assets. Removing the tether has a cascading effect on support infrastructure and ultimately results in a reduction of on-site human support, thereby making operations safer. This new subsea robotics approach helps deliver an economically viable and environmentally sustainable model that significantly reduces operational footprints, operating costs, and GHG emissions, while improving offshore health, safety, and environmental exposure. New approaches to subsea technology could help develop complex autonomous ecosystems and serve as proof of concept for developing commercially viable technologies.