Mobile Robot Path Planning Based on Improved Ant Colony Optimization

Aiming at the traditional Ant Colony Optimization in planning the path of mobile robot, which has the problems of more iterations, long time consuming, low search efficiency, low convergence speed and easy to fall into the local optimal situation, an improved Ant Colony Optimization is put forward: firstly, the concave obstacles in the raster environment are dealt with, which improves the search efficiency of the ant colony in the early stage; secondly, the pheromone negative feedback strategy is introduced into the node transfer formula to further improve the convergence speed and accuracy; finally, the pseudo-random proportional state transfer strategy is added to increase the possibility of selecting the optimal path prematurely while having high global search capability. The simulation results show that the algorithm performs better than the traditional Ant Colony Optimization in terms of the optimal path length and the number of iterations.