Abstract: A mobile robot path planning method based on Quantum particle swarm optimization (QPSO) algorithm is proposed to solve the problem of optimizing the blind search of mobile robots in the solution space, by improving the particle swarm algorithm. Optimizing the convergence speed and population dispersion of individual particles in the particle swarm to complete the dynamic adjustment of the inertia weight to solve the shortcomings of the traditional particle swarm algorithm of premature convergence can make the weight of the QPSO algorithm controllable and adaptive sex. At the same time, the natural selection method is used to update the position of the particle swarm, increasing the diversity of the particle swarm's motion path, and greatly improving the global search ability and convergence speed of the particle swarm. Finally, through the analysis of path planning software simulation and experimental test results, it can be seen that the algorithm is better than the PSO algorithm in the global search ability and convergence speed in the path planning of mobile robots.Among them, the average cost is reduced by about 0.2m and the average time consumption is reduced by 1.28s. This verifies the effectiveness and feasibility of the proposed algorithm.

Key words: position update, Quantum particle swarm optimization algorithm, optimal path, global search