n order to improve search ability of the optimization algorithm, this paper analyzed the basic principles of primary ant colony optimization (ACO) algorithm and reinforcement theory proposed by psychologist B.F. Skinner, introduced positive and negative incentive mechanisms to improve the basic ACO algorithm, and proposed an incentive mechanism improved ant colony optimization (IM-ACO) with giving its mathematical description. The IM-ACO was used to solve traveling salesman problem (TSP) and path planning restricted by both shortest length and collision avoidance with obstacles, whose performances were compared with that of primary ACO. Simulation showed that the IM-ACO successfully achieved the optimal path, fulfilling the global optimization goals. Due to the incentive mechanism, individuals in ant population were able to actively move towards better solutions, and eventually fix the optimal solution more quickly. It was concluded that the IM-ACO lead a faster convergence speed and superior overall performance than the original algorithm did, which were suitable for solving path planning problems.