SeaRobotics Corporation (SRC) has unveiled the company’s next-generation utility-class ASV (autonomous surface vessel), the SR-Utility 3.0. This new unmanned vessel is designed for extended-range coastal marine surveying, and provides professional surveyors with a highly versatile solution that can accommodate a wide range of interchangeable payloads and sensors.
The SR-Utility 3.0 shares the same operator, survey and autonomy controls and proprietary SRC Uni-Cab architecture as the recently introduced SR-Surveyor M1.8, a smaller ASV designed specifically for the surveying of shallow and hard-to-access areas. System compatibility across the different ASV models was a key design principle for the new SR-Utility 3.0
One of the first purchasers of the new system is CSA Ocean Sciences, a US-based marine environmental consultancy firm that already uses a number of other SRC ASV models on large-scale environmental, hydrographic and geospatial survey projects.
Lou Dennis, VP of programs at SeaRobotics, commented: “We have been building ASVs for over twenty years now and understand that there is always a need for some degree of customization when defining form and function, but increasingly we see surveyors demand systems that they themselves are capable of modifying to fit certain applications.”
“The growing appetite for autonomous systems among commercial surveyors calls for ever more flexible and intuitive plug and play platforms like the SR-Utility 3.0, which offers a modular approach to set-up, intuitive interfacing, and—thanks to a shared common Uni-Cab architecture—the option of modular upgradeable expansion to multi-ASV deployments.”
Kevin Peterson, CEO of CSA Ocean Sciences, said: “We are excited about the addition of the SR-Utility 3.0 to our existing fleet of SR-Surveyor M1.8s and the benefits that this breakthrough technology offers to our customers. Having a highly reliable and standardized suite of ASVs helps us maintain the same established crew training, spare parts, and support depots—it is ultimately about optimizing efficiency in the collection of precise data.”
