Use this URL to cite or link to this record in EThOS:
Title: Path planning and collision avoidance of unmanned surface vehicles in the marine environment
Author: Song, Rui
ISNI:       0000 0004 7229 9515
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Efficient maritime navigation with the ability to avoid obstructions is an intensive research topic for autonomous navigation in ‘practical’ Unmanned Surface Vehicles (USVs). However, only few of the existing USVs have applied path planning in their navigation systems. Most studies present validation results at the simulation level and do not consider any environmental disturbances. The aim of this research project is to develop practical and efficient path planning algorithms that can generate and optimise the path based on known (or predicted) traffic and environment data with the ability to adapt to different criteria or missions. New risk assessment strategies together with three novel path planning algorithms have been developed to process and evaluate the real-time environmental conditions, to minimise the adverse effects caused by surface currents, and to improve the safety of the generated path for those circumstances where the reliability of the fused navigational data is uncertain. All these algorithms have been tested and verified in simulations with results proving the effectiveness of path generation and low-cost of energy consumption. Experiments using a practical USV have also been carried out to validate the capabilities of the algorithms.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available