Aiming at problems that convergence speed of particle swarm optimization（ PSO） algorithm is fast but easy to fall into local extremum and the global search capcity of the bacterial foraging optimization（ BFO）algorithm is strong but the efficiency is low

a hybrid algorithm with global search capability and fast convergence by introducing the chemotaxis

migration and replication operations of BFO algorithms into particle swarm search process is proposed. On the basis of introduction of the principle and procedures of the basic BFO algorithm and PSO algorithm

the global path planning simulation experiment of the mobile robot is carried out by using the PSO algorithm

BFO algorithm and the hybrid algorithm respectively

at the same

number of iterations and the fitness curves of each algorithm is given respectively. The simulation results show that compared with the PSO algorithm and BFO algorithm

the proposed hybrid algorithm has the advantages of short search time and fewer iterations

which verifies the feasibility and effcetiveness of the hybrid algorithm in mobile robot path planning very well.