Cleaning Path Optimization For Heat Exchanger Tube Of Vacuum Pan

In sugar industry that hear exchanger tubes of vacuum pan were cleaned manually was dangerous and inefficient in the past. Using robot is general trend and also in line with the requirements of industrial transformation and upgrading of Guangxi. There were as many as tens of thousands of hear exchanger tubes in the vacuum pan, a lot of travel time for no-cleaning is spent, The total travel time when all the hear exchanger tubes have been cleaned by robot is determined by many factors, some of factors exits combination most optimization problem, cleaning path optimization of considered these factors namely is an important measure to reduce total travel time and improve washing efficiency. Robot’s path optimization was studied in this paper, the main contents of this paper are as follow:Differing from the traditional path optimization modeling approach which took the shortest rout of robotic end-effector as objective, cleaning path optimization mathematical model whose objective was that total time for position was shortest was constructed, this model not only took into account rout of robotic end-effector but also considered robotic configuration, so as to establish an objective function which was accurate measure of the total travel time for position.In view of the complexity of cleaning path optimization, in order to speed up calculations and improve the accuracy of solution, that using coarse-grained master-slave parallel genetic algorithm to solve it was proposed. Coarse-grained master-slave parallel genetic algorithm was designed in detail and an adaptive modulating formulae of crossover and mutation probability for all the population was presented. Simulation results showed that, solution accuracy and convergence speed of coarse-grained master-slave parallel genetic algorithm based on adaptive crossover and mutation probability was better than using a fixed crossover and mutation probability.A implementation of coarse-grained master-slave parallel genetic algorithm run on the multi-core CPU+GPU was presented to uttermost speed up the process of cleaning path optimization calculation, and it was implemented detailedly by using MATLAB parallel programming techniques. The implementation fully taps the potential of multi-core CPU and GPU parallel computing in collaboration, makes the best use of speedup of coarse-grained master-slave parallel genetic algorithm. Simulation test, the speedup of coarse-grained master-slave parallel genetic algorithm based on multi-core CPU+GPU was much higher than the speedup of coarse-grained parallel genetic algorithm only just using multi-core CPU, in the cleaning path optimization simulation example, the running time of coarse-grained master-slave parallel genetic algorithm less 62.4 percent less than one of traditional simple genetic algorithm. Optimized cleaning path provided the basis for the cleaning robot control.