Abstract
In this study, a general control framework for autonomous operations in highly complex and dynamically changing environments such as fish farms is proposed and experimentally validated.
Since fish farms feature an environment that includes fish, deformable flexible structures and highly variable environmental disturbances, the framework is designed to interact with these.
The proposed control approach integrates estimates of the cage structure dynamics and fish behavior, adaptive path planning and path following control concepts in one unified and compact framework that could be used to implement and demonstrate different concept studies in dynamically changing environments.
The performance of the control framework is investigated though field trials using a remotely operated vehicle (ROV) in a commercial fish farm. Experimental results show that the proposed framework can be applied to challenging operations in fish farms.
Since fish farms feature an environment that includes fish, deformable flexible structures and highly variable environmental disturbances, the framework is designed to interact with these.
The proposed control approach integrates estimates of the cage structure dynamics and fish behavior, adaptive path planning and path following control concepts in one unified and compact framework that could be used to implement and demonstrate different concept studies in dynamically changing environments.
The performance of the control framework is investigated though field trials using a remotely operated vehicle (ROV) in a commercial fish farm. Experimental results show that the proposed framework can be applied to challenging operations in fish farms.