TY  - UNPB
EP  - 218
A1  - Song, Rui
AV  - public
ID  - discovery10042821
N1  - Unpublished
TI  - Path Planning and Collision Avoidance of Unmanned Surface Vehicles in the Marine Environment
M1  - Doctoral
N2  - 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.
PB  - UCL (University College London)
UR  - https://discovery.ucl.ac.uk/id/eprint/10042821/
Y1  - 2018/02/28/
ER  -