Aiming at the problem of long search time

parameters susceptibility of optimzing process

easy to fall into local extremum

an improved ant colony optimization（ ACO） algorithm is proposed. The improved ant colony algorithm introduces novel update mechanisms of local and global pheromone. The improved ant colony algorithm also introduces’variation’operation into the update mechanism of global pheromone

in order to reduce possibility of ant colony algorithm falling into local optimum. The improved ant colony algorithm is tested aiming at the problem of 2D path planning of mobile robots

and test results of basic ant colony algorithm is used as benchmark.The results show that the basic ant colony algorithm is easy to fall into local converge in advance

so called’prematurity’phenomenon appear

so that it is hard to find the optimal path. By contrast

the improved ant colony algorithm is very robust and it can always find the best path in a short time whether in an easy or complex map

’prematurity’phenomenon doesnot appear.