Research On Hydrodynamic Prediction And Ant Colony Algorithm Path Planning Of Underwater Cleaning Robot

In general,in order to avoid or solve the problems that may occur during the operation of the ship,ships must be regularly docked for inspection,maintenance and cleaning,so that the service life of the ship is extended.Benefit from this kind of demand,underwater cleaning technology has achieved rapid development.Among them,for large hull surface cleaning work,underwater cleaning robot because of its strong practicability and maneuverability and can replace human to complete the job cleaning,this kind of robots is widely used in the field of ship cleaning,in improving human living standards and economic development has an important significance.Hydrodynamic prediction and path planning are important parts of the underwater cleaning robot to move intelligently and flexibly,and are the key links for the underwater cleaning robot to complete the cleaning operation safely,accurately,quickly and efficiently.Firstly,in order to combine the motion state and energy consumption of the underwater cleaning robot in the underwater environment,analyze the hydrodynamic performance of the robot in the underwater environment,mathematical modeling and experimental simulation were carried out to measure the resistance encountered by the underwater cleaning robot during underwater movement.And laid a data foundation for the improvement of the practicality of path planning in the three-dimensional environment and the calculation of path energy consumptionSecondly,carried out the theoretical method research of the path planning technology of the underwater cleaning robot,considering the shortest path,path safety,path energy consumption and other related indicators,the grid method and the ant colony algorithm were selected as the related design methods.With the further deepening of the research,a two-dimensional space model of path planning was established using the grid method,and the basic ant colony algorithm path planning experiment simulation was carried out in this model,and through the experimental results,it is known that the algorithm has disadvantages such as easy to fall into the local optimal trap and slow convergence speed.In order to solve this kind of drawbacks,this article improves the ant colony algorithm from three aspects:the introduction of the direction heuristic factor,the adaptive adjustment of the pheromone volatilization factor,and the pheromone update rule of the high-quality path.Comparing and analyzing the experimental results of the improved ant colony algorithm and other ant colony algorithms in different environments,it is concluded that the optimal path searched by the improved ant colony algorithm in this thesis is not only shorter and fewer inflection points,but also can make the convergence faster and have more High feasibility and effectiveness.Finally,aiming at the research on the three-dimensional path planning of the improved ant colony algorithm,the space model of the three-dimensional path planning was built by the grid method again,the model was divided into planes,and each plane was rasterized.The pheromone storage method and path search mode had been designed,and the pheromone storage method was changed to the path location node storage,and the hierarchical search method was adopted,which reduces the storage space of the pheromone.Based on the improvement of the two-dimensional path planning of the ant colony algorithm,in order to make the improved algorithm more suitable for solving practical problems,combined with the previously calculated characteristics of the resistance encountered by the robot,the design practicality factor is constructed into a new multivariate heuristic function.The experimental simulations of the improved ant colony algorithm and other ant colony algorithms were comprehensively measured from the three aspects of the number of turns,the energy consumption of the path,and the fitness of the path.The simulation results showed that the improved ant colony algorithm can plan the optimal path with high smoothness,low energy consumption and more practical application in the three-dimensional complex environment,which verifies the effectiveness and superiority of the improved ant colony algorithm in this thesis in dealing with the three-dimensional path planning problem.