Global path planning based on improved ant colony optimization algorithm for geometry

The improved ant colony algorithm and path geometry optimization were applied to solve the global path planning problem of mobile robot. The obstacle performance was combined in the proposed algorithm to establish the workspace model of the robot. By setting the initial pheromone, the ant searching speed was accelerated, and through the adaptive pheromone mechanism, the interference problem of initial pheromone to the specific map was solved. In addition, the pros and cons of the path planning were screened by setting the adaptive path length. It was also proposed that the pheromone spreading strategy was decided by the path length. Meanwhile, according to the principle of geometry, the planning path was optimized to accelerate the convergence speed of the optimal solution. The effectiveness and universal application of the proposed algorithm was demonstrated by the simulation results. In the random environment map, the optimal path could be rapidly obtained with the proposed algorithm.