Research On Mobile Robot Path Planning With Particle Swarm Optimization Algorithm And The Ant Colony Algorithm

People pay great attention to the research and development of mobile robots in recent years. For the requirement of its higher ability to take corresponding measures in accordance with the environmental changes in the moving, the path planning plays a very important part in the field of robot intelligence. The task of path planning is to find a path without touching the existing obstacles from the initial state to the goal state according to certain evaluation criteria. Particle swarm optimization algorithm and ant colony algorithm are typical swarm intelligent algorithms in the field of path planning algorithm, following the fuzzy method, neural networks and genetic algorithm.The research of the thesis is as follows:The thesis analyzes the deficiencies of the Particle swarm optimization algorithm and Ant colony algorithm from the perspective view of theory. Then, the PAAA, i.e., the integration of the Particle swarm optimization algorithm and Ant colony algorithm is put forward in order to improve them. At first, the PAAA uses the Paticle swarm algorithm's strong global search ability to generate the pheromone distribution, and then uses the ant colony algorithm's feedback mechanism to get the exact solution. Finally, the PAAA simulate in MATLAB. The simulation result is compared with Particle swarm optimization algorithm and Ant colony algorithm respectively to prove the effectiveness of the PAAA.The PAAA is used in the dynamic path and static path planning respectively. The simulation result shows that the search time of PAAA is shorter than that of genetic algorithm and the search result of PAAA is better than PSO in the same environment.The simulation results show that the application of the PAAA to the path planning of mobile robot improves the present algorithm. It provides us the possibility of exploring a new method for path planning.