Efficient maritime navigation through obstructions is still one of the many problems faced by mariners. The increasing traffic densities and average cruise speed of ships also impede the collision avoidance decision making process by reducing the time in which decisions have to be made. It seems logical that the decision making process be computerised and automated as a step towards reducing the risk of collision. Although some studies have focused on this area, the majority did not consider the collision regulations or environmental conditions and many previously proposed methods were idealistic. This study develops a motion planning algorithm that determines an optimal navigation path for ships in close range encounters based on known and predicted traffic and environmental data, with emphasis on the adaptability of the algorithm to optimised for different criteria or missions. The domain of interest is the 5 nautical mile region around own-ship based on the effective range of most modern navigation radars and identification devices. Several computational constraints have been incorporated into the algorithm and categorised based on safety priority. Collision-free and conformity with collision regulations are the primary constraints that have to be satisfied; followed by secondary or optional mission specific constraints e.g. commensurate with environmental conditions or taking the shortest navigation path. Own-ship speed is considered to be a dynamic property and a function of the engine setting, which is a variable modifiable by the optimisation routine. The change in the ship’s momentum as a result of a turning manoeuvre is also included in the model. A modified version of an evolutionary algorithm is adopted to perform the optimisation, where the variables are spatial coordinates and the engine setting at the particular path segment. The navigation path can be optimised for specific criteria by adjusting the weighting on the cost functions that describe the properties of the navigation paths.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625179 |
| Date | January 2009 |
| Creators | Tam, C. K. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/17267/ |
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