Robot Path Planning Based On Ant Colony Optimization Algorithm In Three-Dimensional

With the development of science and technology, robot technology has been gradually penetrated into many fields of human life, and in the development of robot technology, the research of robot path planning technology is a very important and very critical aspect. And the development of robot technology speed depends on the depth of the path planning technology research. At present, the technology of two-dimensional planar of robot path planning have been relatively matured, but the research of robot path planning technology in three-dimensional terrain environment still needs more in-depth research. As the three-dimensional terrain environment is imitate the actual living space of human being, which with a lot of random situations, and it is difficult to measure in advance, which led to the current situation of the robot path planning in three-dimensional terrain is difficult to spread. Based on this problem, this study uses ant colony algorithm for path planning, and applies it robot path planning in three-dimensional terrain.Because the ant colony algorithm is a bionics algorithm, so there are still some shortcomings of its own,, such as slow convergence speed, easy to fall into traps and local optimum of the environment. In order to make sure the ant colony algorithm can be used in a three-dimensional terrain robot path planning problem, this paper presents four optimization strategies to improve the ant colony algorithm, and make simulation and contrast in the two-dimensional planar environment respectively. The results show that anti-potential field method avoids the ant trapped into local optimum and stagnation, hybrid optimization strategy improves the global search ability of ant colony algorithm, adaptive strategy to enhance the stability of the algorithm; the survival of the fittest way will accelerate the convergence speed of the algorithm.In view of each single optimization strategy has its own problems; this paper takes the measure of combing these optimization strategies together, and learns from each other in the research of the robot path planning in three-dimensional terrain. First, the three-dimensional terrain environment model is made. And then the process of ant colony optimization algorithm is designed. When the ant trapped into the trap in the process of path searching of environment model; The use of the anti-potential field method makes the ants can come out from it and avoids the ant colony stagnation problem; At the end of each round, during a global pheromone updating, using hybrid optimization and adaptive adjustment strategy, this can not only guarantee the global search ability of the algorithm, but also increases the probability of optimum path being selected as the optimal path; Because of these three strategies will increase the time-consuming of the algorithm, which is not conducive to the rapid convergence of the algorithm, so the survival of the fittest way will accelerate the convergence speed of the ant colony algorithm. Finally, in the three-dimensional terrain environment simulation models shows that the performance of ant colony optimization algorithm combined with these strategies has been greatly improved than ant colony algorithm. Not only the quality of the optimal path has been improved, but also has good convergence. Proved that the feasibility and effectiveness of the optimization algorithm in the three-dimensional environment model simulations, and has some theoretical value and practical significance.