Optimal path planning of mobile robot based on improved ant colony algorithm

In the current research field of mobile robots, optimal path planning is a hot topic. Ant colony algorithm is diffusely used in mobile robot path planning because of its its positive feedback, distributed and so on. However, traditional basic ant colony algorithm has some disadvantages, such as easy local convergence, slow search convergence rate and low efficiency. To solve these problems, this paper proposes an improved ant colony algorithm. Based on the algorithm principle, a new method for global pheromone initialization is put forward by improving and optimizing the pheromone mechanism, and there is a certain probability to carry out “variation” operation in each search iteration process. Meanwhile, the calculation method of heuristic function is improved to reduce the possibility of premature convergence of the algorithm falling into local optimum. The two-dimensional space model of mobile robot is created by grid method. Then they are simulated and tested respectively, and the test results are compared and analyzed. The experimental results show that the improved algorithm can obviously optimize the defects of the original algorithm, which not only can plan the path faster, but also can plan the better path, and the search efficiency has been significantly improved.