A study of a coordinated path planning strategy for a fleet of unmanned surface ships in the presence of static obstacles

This thesis addressed the problem of path planning for a fleet of unmanned surface ships in the presence of static obstacles. The aim of this study is to propose a new strategy to avoid potential collisions with static obstacles and each ship. In addition, the objective of the proposed strategy is to enable the fleet to autonomously change an initial formation shape into a safer formation shape when the fleet of the ships faces with static obstacles. A core idea adopted for the proposed strategy was motivated by behaviours of school of fish in the ocean.;We assumed that the behaviours of school of fish follow three rules. These assumptions were extended to main methodology of this study: 1) Path planning for a leader ship (Potential Field Method), 2) Formation control for a fleet (Consensus algorithm) and 3) Path planning strategy for follower ships of a fleet. In addition, Potential Field Method and Consensus algorithm were integrated to develop the new path planning strategy, which imitates the motion of school of fish when obstacles are encountered.;Repulsive force vectors obtained from results of Potential Field Method were used as parameters of proposed mathematical equations to change formation shapes of the fleet. In order to validate the proposed strategy, simulations have been carried out for five unmanned surface ships modelled as point mass models with full actuation under MATLAB/Simulink environment. Simulation results illustrated that the fleet of the five ships changed their formation shape into a safer formation shape in the presence of user-designed static obstacles and the fleet of ships successfully passed through the static obstacles without collisions with each ship.;From these simulation results, this study has demonstrated that this proposed strategy will contribute to safety and automation of multiple unmanned surface ships in the presence of static obstacles.

Name dc:type.qualificationname

mphil

Level dc:type.qualificationlevel

masters-pg

Grantor dc:publisher.institution

University of Strathclyde

Year dc:date.issued

2018

Author dc:creator

• Jeon, Maro

Advisors dc:contributor.advisor

• Clelland, David
• Kim, Sang-Hyun

DOI

10.48730/6j9b-4167

Identifier

T14921

Author Identifier

201785644

OAI identifier oai:identifier

oai:strathclyde:4f16c2806