Energy efficient path planning and model checking for long endurance unmanned surface vehicles.
| dc.contributor.advisor | Savvaris, Al | |
| dc.contributor.advisor | Tsourdos, Antonios | |
| dc.contributor.author | Niu, Hanlin | |
| dc.date.accessioned | 2023-04-06T11:13:55Z | |
| dc.date.available | 2023-04-06T11:13:55Z | |
| dc.date.issued | 2017-09 | |
| dc.description.abstract | In this dissertation, path following, path planning, collision avoidance and model checking algorithms were developed and simulated for improving the level of autonomy for Unmanned Surface Vehicle (USV). Firstly, four path following algorithms, namely, Carrot Chasing, Nonlinear Guidance Law, Pure pursuit and LOS, and Vector Field algorithms, were compared in simulation and Carrot Chasing was tested in Unmanned Safety Marine Operations Over The Horizon (USMOOTH) project. Secondly, three path planning algorithms, including Voronoi-Visibility shortest path planning, Voronoi-Visibility energy efficient path planning and Genetic Algorithm based energy efficient path planning algorithms, are presented. Voronoi-Visibility shortest path planning algorithm was proposed by integrating Voronoi diagram, Dijkstra’s algorithm and Visibility graph. The path quality and computational efficiency were demonstrated through comparing with Voronoi algorithms. Moreover, the proposed algorithm ensured USV safety by keeping the USV at a configurable clearance distance from the coastlines. Voronoi-Visibility energy efficient path planning algorithm was proposed by taking sea current data into account. To address the problem of time-varying sea current, Genetic Algorithm was integrated with Voronoi-Visibility energy efficient path planning algorithm. The energy efficiency of Voronoi-Visibility and Genetic Algorithm based algorithms were demonstrated in simulated missions. Moreover, collision avoidance algorithm was proposed and validated in single and multiple intruders scenarios. Finally, the feasibility of using model checking for USV decision-making systems verification was demonstrated in three USV mission scenarios. In the final scenario, a multi-agent system, including two USVs, an Unmanned Aerial Vehicle (UAV), a Ground Control Station (GCS) and a wireless mesh network, were modelled using Kripke modelling algorithm. The modelled uncertainties include communication loss, collision risk, fault event and energy states. Three desirable properties, including safety, maximum endurance, and fault tolerance, were expressed using Computational Tree Logic (CTL), which were verified using Model Checker for Multi-Agent System (MCMAS). The verification results were used to retrospect and improve the design of the decision-making system. | en_UK |
| dc.description.coursename | PhD in Aerospace | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19419 | |
| dc.language.iso | en | en_UK |
| dc.rights | © Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | Collision avoidance | en_UK |
| dc.subject | path following | en_UK |
| dc.subject | path planning | en_UK |
| dc.subject | energy efficient | en_UK |
| dc.subject | spatially-temporarally variant sea current | en_UK |
| dc.subject | model checking | en_UK |
| dc.subject | decision making | en_UK |
| dc.title | Energy efficient path planning and model checking for long endurance unmanned surface vehicles. | en_UK |
| dc.type | Thesis | en_UK |