A quantum evolutionary algorithm for robot path planning in dynamic environment

In order to improve the searching efficiency of the real-time path planning problem for mobile robots, an improved quantum evolutionary algorithm is proposed. By using the grid method to build the environmental model, a novel decoding method which transforms the quantum individual into the path described by grid points is presented. On the basis of the quantum rotation gate, a repair operation specifically designed for the path planning problem and the cross and mutation operators in genetic algorithms are introduced to update the quantum population together. With the help of GUI in Matlab, process of the robot real-time path planning has been simulated. Simulation results indicate that the proposed method can obtain a feasible and short path in the complex environment. When a new obstacle appears suddenly, or the original ones move towards different directions, this method can also respond quickly and re-plan an optimal path in the new environment. Besides, comparisons with other path planning methods demonstrate that the given algorithm has the characteristics of high stability and robustness.