At present, it is difficult to perform optimized operations with respect to time, costs and operational efficiency describing the complex and dynamic environment of fish cages (up to 1000 t fish in 50.000 m3), while there is increased trend for use of remotely operated UUVs in fish farms for a variety of applications (e.g. inspection of structures and nets, net cleaning and repair, monitoring of fish health and behaviour). However, in order to increase objectivity and efficiency during such operations, it is crucial to develop solutions where the UUVs operate autonomously with limited human involvement.
The ResiFarm envisions to reshape the underwater operations in dynamic, complex and perceptually-degraded environments by developing new knowledge and novel technology to enable resilient autonomy for Unmanned Underwater Vehicles (UUVs). ResiFarm challenges the resilience in one of the most demanding industrial environments such as fish farms. Motivated by the core hypothesis that there exists a broadly defined and unified science for resilient autonomy of UUVs operating in complex, high risk, perceptually-degraded and dynamic environments, the envisioned research will be holistically organized around three cross-cutting objectives that are addressing the current knowledge gaps on: a) resilient multi-modal perception for UUVs operating in dynamic perceptually-degraded environments, b) methods for novel motion planning concepts that enable safe, fish- and structure-aware operations and c) validation of the fully integrated system in field studies.
By utilising the competence of the interdisciplinary team (SINTEF, NTNU), industry partners' (Eelume, Skrav Technologies) expertise in the underwater robotic and automation domain, and involving international experts in topics relevant to the project (MIT, LSTS, TUM, ETH), our team aims to provide the foundation for the new generation of permanent resident UUVs that co-exist with fish without causing negative impact and autonomously navigate and interact with flexible structures. The result of this project will be the new science and systems to facilitate long-term autonomous underwater operation of UUVs and promote sustainable expansion in fish farms and other maritime industries such as fisheries, subsea oil and gas and offshore wind farms. Overall, ResiFarm will impact research communities, businesses, the public sector and society at large through collaboration between these and with the outmost dedication to developments and demonstrations of novel methods within artificial intelligence, automation and robotics.
The primary objective of ResiFarm is to develop fundamental knowledge and technology for underwater perception and motion planning that enable resilient autonomy for Unmanned Underwater Vehicles (UUVs) during high-risk operations in complex fish farm environments involving thousands of fish and deformable structures.
Secondary research objectives are:
- Develop methods for resilient multi-modal perception of underwater robots enabling localization, mapping and autonomous navigation in dynamic perceptually-degraded settings such as fish farms.
- Develop methods for safe and cognizant motion planning for online operations that enable collision-free navigation in dynamic fish farm environments, without impacting the fish.
- Validate the fully integrated system in field studies by demonstrating autonomous net cage and mooring line inspections and intervention operations in close collaboration with industry partners.