The shift to sustainable energy sources has increased demand for Energy Transition Metals such as nickel, copper, cobalt, and manganese. To satisfy this need while reducing the negative social and environmental effects of conventional mining, Deep-sea Nodule Collection (DSNC) appears to be a feasible option. Despite its potential, DSNC encounters difficulties with path planning since it depends on imperfect global paths, which result in inefficiencies and safety issues. This thesis looks into the possibility of using a local path planning algorithm, specifically an Interpolating Curve Planner (ICP).

To assess the ICP's performance under various model hyperparameters and environmental scenarios, a simulation tool is created. The study examines how well the ICP performs in terms of area coverage, collection rate, and reliability along different hyperparameters and presurvey measurement resolutions. It integrates novel production rate objectives, previous track following, and real-time sensor measurements within an environment generated from bathymetric data.

The examined local path planner shows a step towards optimised DSNC. Further research is needed to refine objectives and the structure of the local path planning algorithm to maximize its potential in addressing DSNC challenges effectively.