Energy-optimal model predictive control for unmanned underwater vehicles in offshore aquaculture fish net-pen visual inspection

Abstract

• The energy-optimal Model Predictive Control problem formulation that reflects the real power consumption is proposed. • From theoretical and practical aspects, solving the resulting non-convex optimization in real-time is demonstrated. • The effects of non-convex system-level power function on energy optimization is investigated. • The controllers are tested for real-time feasibility in high-fidelity simulation using ROS and Gazebo taking into account the fish farm and UUV specifications. • The proposed EO-MPC saves 3.1 % - 21.4 % more energy than CO-MPC while achieving better or equivalent trajectory tracking performance under different underwater current disturbance speeds ( 0.0 m / s , 0.5 m / s and 0.9 m / s ). Unmanned underwater vehicles are deployed to automate the production processes in offshore aquaculture, but the onboard power supply with limited energy capacity constrains the operational range and time. In this paper, a nonlinear energy-optimal Model Predictive Control (EO-MPC) is proposed to perform a 4-degree-of-freedom 3D fish net-pen visual inspection trajectory tracking while minimizing energy consumption. The EO-MPC problem with explicit energy-related terms in the performance index (PI) is transcribed into a nonlinear programming problem (NLP), solved via IPOPT, the open-sourced primal-dual interior point solver. Using the specifications of Blue Endeavour Project (the upcoming first-of-its-kind offshore salmon firm in New Zealand) of the New Zealand King Salmon Company and the work-class ROV called RexROV 2, theoretical fundamentals and practical implementation aspects are detailed, and four controllers are tested in high-fidelity simulation using Robot Operating System and Gazebo Physics Engine. In a general constrained operational working environment, the proposed EO-MPC controller saves 3.1 % - 21.4 % more energy than the conventional MPC (CO-MPC) while achieving better or equivalent trajectory tracking performance under different underwater current disturbance speeds ( 0.0 m / s , 0.5 m / s and 0.9 m / s ).