Research On Path Planning Algorithm Of Wave Dynamic Glider Based On Improved Potential Field Ant Colony Algorithm

The wave glider(WG) driven by wave energy is a new marine environmental monitoring platform, which has an epoch-making significance in the field of international marine environmental technology today. The wave glider depends on its unique double-body structure to convert wave energy into the forces propelling it forward, which makes up the shortfall that the traditional marine monitoring tools need refueling on a regular basis. It not only saves energy, reduces cost, but also has high cruising ability and environment adaptability.However, the complexity of marine environment and the power source characteristics of wave glider make the path planning of wave glider particularly difficult.And the traditional path planning algorithm cannot meet the navigating demand of wave glider. Therefore in order to plan out the optimal navigation path, where the speed of wave glider is fast, the time spent is short and there is no collision with obstacles, it is necessary to design a kind of new algorithm in view of the characteristics of wave glider.There are a lot of the traditional path planning algorithms, such as simulated annealing algorithm, genetic algorithm, tabu search algorithm, ant colony optimization algorithm, and so on. However, compared with other path planning algorithms, the ant colony optimization algorithm(ACO) has a big advantage, which makes the ACO suitable for planning the path of wave glider. The big advantage is that the ant colony optimization algorithm can dynamically respond to the changes of external environment by the positive feedback mechanism and improve the computational efficiency by the parallel computation mechanism. So select the ant colony optimization algorithm as the basis algorithm for planning the path of wave glider.Because the speed of wave glider is entirely dependent on the surrounding environment, the traditional ant colony optimization algorithm need be improved. Firstly,the algorithm introduces artificial potential field(APF) force into heuristic information to ant colony optimization with potential field(ACOPF) for overcoming the disadvantages of ant colony optimization algorithm. Secondly, take into consideration the main environmental factors which affect WG speed. Thirdly, improve the update strategy of pheromone during iteration by means of elitist strategy. Finally, change the WG path in real time according to the drift of dynamic obstacles. The improved ant colony optimization with potential field will become the path planning algorithm of wave glider.In order to verify the performance of the improved ant colony optimization with potential field, set up some different marine environment models with grid algorithm. The simulation results show that the improved ant colony optimization with potential field not only applies for different marine environments, but also searches the optimal path by synthesizing distance, time and obstacles avoidance according to the characteristics ofwave glider. The experiment validates the practicability and validity of the hybrid algorithm...