Energy-aware USVs path planning

Abstract

Unmanned Surface Vehicles (USV) are an innovative solution for various maritime applications such as marine navigation, rescue, environmental monitoring and surveillance, etc. USVs offer the advantage to operate in hostile or dangerous environments where humans cannot safely or not at all perform. In general, USVs operate in harsh environmental conditions that require accuracy, reliability and autonomy. To meet these critical requirements, the focus on USVs and their applications is gradually performed. One of the most important problems to be solved is that of trajectory planning. In order to execute the planned tasks, the USVs must operate in an autonomous way and manage their resources optimally in order to minimize human interventions. Thus, performance and autonomy criteria are very important to consider when executing any type of task. In this thesis, we address the general problem of maritime surveillance using a USV equipped with an on-board LiDAR (Light Detection and Ranging) that allows remote coverage of distant points. The objectives are to cover the maximum area with lowest energy cost while avoiding collisions with obstacles. To solve this problem, we used two optimization approaches: • The first one consists in using heuristic methods based on multi-objective evolutionary algorithms. In this case, two algorithms are used and compared. One consists of a local search method known as Pareto Archived Evolution Strategy (PAES). Other consists of a population-based search algorithm called Non-Dominated Genetic Sorting Algorithm II (NSGA-II). • A novel method is proposed to improve the performance of evolutionary algorithms when solving path planning problems by reducing the size of chromosomes. • The second approach isbased on the exact method using a Mixed Integer Programming (MIP) model with two objective functions inspired by both the Covering Salesman Problem (CSP) and the Travelling Salesman Problem with Profit (TSPP).