Planning a collision avoidance model for ship using genetic algorithm

Using genetic algorithms to plan the safe path for ship in congested traffic situation, a new gene vector is proposed. The gene vector is composed of the position and speed of our ship, as well as a noise model. The noise model describes the influence on a maneuvering ships system of wind, sea waves and the other natural factors. To test and verify the new gene vector, the equipment installed on "Shioji Maru" (the training ship of our university) have been applied to an automatic collision avoidance system. In the experimental system, the ARPA (Automatic Radar Plotting Aids) system was used to collect information on the navigational obstacles around our ship. The information was processed. Useful information, especially that related to target ships, was extracted and used to derive a stochastic predictor that can predict the future position and degree of future collision threat in sufficient time. The information relating to our own ship was detected with GPS and other sensors. These data were introduced to a GA optimum controller. The optimum or semi-optimum path was evolved from a set of possible safe paths based on the fitness function. Many experiments have been done and the results are presented