Underwater robots are essential for inspecting oil rigs, but avoiding collisions is critical to protect both the robots and the infrastructure. This project developed smart sensing and control systems that help underwater robots safely navigate complex environments—reducing risk, cost, and downtime.
Project Significance & Impact
Remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are vital tools for offshore oil and gas operations. But if they collide with each other or with subsea structures, the damage can be costly and dangerous. This project developed a new system using magnetic signals and computer vision to help these robots detect and avoid obstacles in real time.
By improving how underwater robots sense their surroundings and share data, this research makes subsea inspections safer, more efficient, and more reliable. It also lays the groundwork for future robotic swarms that can work together in high-risk environments without human intervention.
Project Outcomes
Project Details
ROVs are high-value assets sent to areas difficult to access. Consequently, they are used in the oil and gas industry for tasks which can be dangerous for human personnel, such as rig inspection. Collision avoidance is a paramount concern to protect both subsea assets and the robots themselves. This is necessary, because servicing an ROV stranded subsea would require rescue missions that scale in complexity. In addition, AUV swarms require low-cost, robust methods to avoid agent-agent collisions.
The Robotic Swarm Control Lab and collaborators have designed and tested tri-axial antennas for underwater AUVs and ROVs [1]. Pairs of these antennas could be implemented to rapidly measure relative 6-DOF range and orientation between pairs of AUVs and/or AUVs and underwater assets.
