A Quadratic path planning algorithm based on sliding window and Ant Colony Optimization (QACO) algorithm was put forward on the issue of weak planning ability of Ant Colony Optimization (ACO) algorithm in complex environments. The feedback strategy of the ACO based on Feedback Strategy (ACOFS) algorithm was improved, and the feedback times were reduced through the decrease of pheromone along feedback path. In the first path planning, the improved ACO algorithm was applied to make a global path planning for the grid environment. In the second path planning, the sliding windows slid along the global path. Local path in sliding windows was planned with ACO algorithm. Then the global path could be optimized by local path until target location was contained in the sliding window. The simulation experiments show that, the average planning time of QACO algorithm respectively reduces by 26.21%, 52.03% and the average length of path reduces by 47.82%, 42.28% compared with the ACO and QACO algorithms. So the QACO algorithm has a relatively strong path planning ability in complex environments.