• Autonomous Underwater Vehicle
• Gaussian Processes
• Genetic Algorithm
• Informative Path Planning

In the last decades, sea protection and monitoring have been a central topic in the underwater robotics world. Nowadays, autonomous underwater vehicles (AUVs) usage overcame many mission quality limitations related to inefficient systems employment, but intelligent AUVs use remains the central problem. In this regard, it has been developed a path planner based on a genetic algorithm for information gathering to Posidonia oceanica (PO) meadows monitoring. PO is an endemic plant of the Mediterranean Sea that provides a wide variety of ecosystem services to society, contributing to coastal protection, water quality and fisheries. Unfortunately, due to its coastal location, it suffers numerous human pressures that threaten its conservation, therefore it needs recursive monitoring employing optimised methodologies to increase surveys efficiency and the quality of related information to compare them over time. Hence, the genetic planner moves in a continuous environment without a prefixed destination, establishing the path aim in higher uncertainty areas defined through a Gaussian Process (GP). It will be provided an overview of Informative Path Planning (IPP), GPs and genetic theory, useful for contextualising the planner and genetic operators implemented. Then results, conclusions and future works will allow a suitable framework to appreciate how the work improves information gathering during monitoring surveys.