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Recently, there has been an increased focus on developing an autonomous shipping technology that is safe, trustworthy, and efficient. Some enablers of this technology are strategies to advance sustainability and reducing CO2 emissions. This is achieved by making ships more efficient, increasing safety and reducing the number of accidents caused by human errors on water, and reducing operational costs. However, there are still many challenges to face before autonomous technology on the water becomes a part of our daily life. A safe and reliable path planning and collision avoidance method is an important component in autonomous shipping and plays a key role in incorporating this technology into our daily lives. Although numerous path planning algorithms for autonomous vessels have been developed, each with its own benefits and limitation, there is no one ultimate path planning and collision avoidance algorithm that is suitable for every vessel, in all water regions and in all scenarios. There is also no unified way of evaluating and comparing these algorithms to find the most suitable one for the chosen use case. In this context, the main purpose of this research is to propose a strategy for a unified evaluation and comparison of path planning and collision avoidance algorithms. To achieve this goal, it is essential to first gain an understanding of path planning and collision avoidance as a part of the autonomous surface vehicle’s guidance, navigation, and control system. There are two main application cases. First, it could be used as an offline benchmarking tool to evaluate and compare the algorithms. Second, it could be used online on an actual vessel to select the safest and most efficient path in the planning phase based on the current situation. This thesis presents an evaluation simulator platform (ESP) for evaluating path planning and collision avoidance algorithm performance for autonomous surface vehicles. In particular, the work focuses on an extended collision risk assessment (ECRA) method for evaluating the generated paths from a safety perspective. The testing results indicate that the proposed approach could be used for autonomous path planning and collision avoidance algorithm evaluation and comparison with some improvements. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of NTNU’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.

Hovedmålet med denne oppgaven var å designe en kollisjons-unngående hybrid-algoritme basert på Velocity Obstacle og Multi Objective Particle Swarm Optimization. Formålet var å forbedre ytelsen i områder som anses om svakheter i de originale algoritmene. Både original- og hybrid-algoritmene ble kjørt gjennom en rekke tester, som viste at hybridalgoritmen viste bedre ytelse i visse områder i forhold til algoritmene den var basert på. The main goal of this thesis was to design a hybrid collision avoidance algorithm from components of the Velocity Obstacle and Multi Objective Particle Swarm Optimization algorithms. The purpose was to improve the performance in areas that were weaknesses in the original algorithms. The original and hybrid algorithms were run to a selection of tests, which found that the hybrid algorithm improved performance in certain areas compared to the algorithms it was based on.

Autodrone 2021 er en årlig konkurranse arrangert i Horten. Den har som mål å etablere og styrke den maritime utdanningen nasjonalt. Av den grunn ønsker NTNU Ålesund å ha en tilstedeværelse i konkurransen og følgende rapport er for å støtte opp om dette. NTNU Ålesund deltok for første gang i fjor (2020). På grunn av den pågående pandemien ble den fysiske konkurransen avlyst. Erfaringene tilegnet av fjor årets gruppe, la grunnlaget for arbeidet vi har gjort gjennom prosjektet. Vi satte oss et mål om å ha en fungerende drone som møter de kravene som er satt av arrangørene, i tillegg vil det bli implementert et system for objekt deteksjon samt et system for å navigere dronen autonomt. Alle systemene er designet med fokus på det modulære. Dette for å fasilitere implementeringen av oppgraderinger for fremtidige grupper. The Autodrone 2021 is an annual national competition hosted in Horten. Autodrone aims to establish and strengthen the maritime higher education in Norway. Therefore NTNU Aalesund is interested in being a part of the competition, and this thesis is to support upon this interest. Last year (2020) was NTNU Aalesund’s first year participating in the competition, but due to covid-19 restrictions, the physical competition got canceled. Nevertheless, last year’s group gained important experience and laid the foundation for our vessel design. By moving forward with the work of last year’s group, we set the goal to have a functioning vessel that meets all the requirements for the competition. In addition we will implement a vision system to localize and detect objects and a system for the vessel to navigate autonomously. All while keeping focus on modular solutions. This is important as it would make it easy for future groups to implement changes.