Aquaculture Robotics: Adaptive Path Planning Through Real-Time Estimation of the Shape of Flexible Net Pens

Abstract

Aquaculture is a marine industry experiencing significant growth and an important seafood provider. Underwater vehicles such as remotely operated vehicles (ROVs) are commonly used for inspection and maintenance of the net pens where the fish are grown. These net pens are flexible structures whose position and shape change with ocean currents and waves. Any autonomous robotic operation in aquaculture is therefore challenging as the net pen position and shape cannot be predetermined and since it is imperative that the robot does not collide with and damage the net. This article addresses this issue by proposing a novel method to estimate the full shape of aquaculture net pens in real time using an underwater vehicle equipped with a forward-looking Doppler velocity log. The method introduces a new concept for how sparse measurement data on the net pen can be fused with numerical models of the full net pen that contrasts other models in literature by not requiring instrumentation on the net pen nor knowledge of ocean current conditions. The estimator output is then used in closed-loop vehicle control by planning and following paths relative to the estimated pen shape. The method is tested in simulations, which show an root mean square error (RMSE) of 0.5 m for estimate of the entire net pen structure and centimeter-level estimation error of the distance between the vehicle and net, and in full-scale trials in an industrial fish farm where an ROV autonomously navigated a net pen.

Aquaculture Robotics: Adaptive Path Planning Through Real-Time Estimation of the Shape of Flexible Net Pens