Research On Mobile Robot Path Planning Based On Improved Ant Colony Algorithm

Path planning for mobile robots is one of the core contents of the filed of robotics research with complex, restrictive and nonlinear characteristics. The ant colony algorithm (ACA) is a new bionics optimization algorithm developed in the past decade. It shows excellent performance and great potential for development when solving many complex problems. In this thesis, it mainly discussed global path planning problem for mobile robots based on ACA in static environment.Firstly, grid method is used to establish the environment model and the way to choose the next point and the pheromones updating strategy is also put forward. Basic ACA is used to plan the path in two different environments. It proved the effectiveness of basic ACA and also showed some problems of it.Secondly,It improved the performance of the basic ACA. First, it optimized the parameter of basic ACA using genetic algorithm. Second, The Max-Min Ant System is used in the designation. Third, the strategy of withdrawal is used to prevent the ants falling into the trap. Fourth, it added oriented functions to the program in order to enhance the ant algorithm efficiency and reduce the time complexity.Finally, in order to verify the effectiveness of improvement measures, it simulated for two different maps. The simulation result shows that the improved ant colony algorithm has better optimization ability and stability. It proved that the improved ant colony algorithm is effective and feasible.