Research On Path Planning Of Cleaning Robot Based On Cluster Partition And Improved Ant Colony Algorithm

Smart cleaning robot incorporates a number of artificial intelligence technology, widely used in homes, public places, offices and other environments to semi-automatic or automatic cleaning. Global path planning is intelligent cleaning robots, one of the most important technology, to a certain extent, marking intelligent cleaning robot intelligent level. Cleaning robot global path planning requirements in the case of the known environmental information, in order to minimize the cost(such as the shortest path, the time at least, the lowest energy consumption, etc.) of a plan covering the global optimal or optimum route, in this process, to avoid collision between the robot and an obstacle occurs. Ant colony algorithm as one of the most common path optimization algorithm has simple and easy to implement, etc., have been used to solve many researchers to be intelligent cleaning robot path planning. When using the ant colony algorithm to solve the problem of cleaning robot path planning, ant colony algorithm is easy to converge to a local optimal solution when solving large-scale problems and inefficiencies shortcomings. To solve the above problems, the main research work are as follows:firstly, the grid method is used to model the working environment of the cleaning robot. Then taking into account the shortcomings of the Path optimization algorithm in the face of the concave obstacles easily fall into the local optimal solution. The obstacles on the known raster map use digital image processing inside the corrosion(Erosion), expansion(Dilation) pretreatment. Such that the concave obstacle is turned into a rectangular obstacle, so the ant colony algorithm can avoid the local minimum solution.Secondly, considering the ant colony algorithm to solve the problem of large scale traveling salesman problem, the efficiency is low, in this paper, we propose a method of using Path optimization algorithm for the path planning of the grid area. In this paper, the K-Means clustering algorithm and support vector machine(SVM) are combined to make the grid map is divided into several regions by the different constraint conditions. Then, ant colony algorithm is used to optimize the path of the grid region, which makes the total efficiency of the ant colony algorithm significantly increased, and the total convergence rate of the algorithm is greatly improved.Finally, Ant colony algorithm to solve the cleaning robot global path planning, having easily converge to a local optimal solution when solving large-scale problems and inefficiencies shortcomings presented Optimizing ant colony algorithm. The algorithm uses a pseudo-random proportional selection method on the path selection rule; using the pheromone update strategy local pheromone update and global pheromone updating a combination of methods; using(4-optimization) method on the local search approach to the local search; makes the algorithm global search capability and efficiency have been improved. When using this method for a path to solve the path planning, obstacle occurs through the phenomenon of using LB(Liang-Barsky) algorithm with clipping path intersecting the obstacle grid, and then the path is corrected to get a global final cleaning robot optimal path. The simulation results show that this method can achieve efficient cleaning robot global path planning.