Research On Path Planning Of Mobile Robot Based On Intelligent Algorithm

Since the 21st century, the development of the mobile robot is very quick, and it gradually penetrates into all areas of human life. Path planning is an important manifestation of the intelligent of the mobile robot. Aiming at the path planning problem of the mobile robot, the paper uses different algorithms.Firstly, in order to solve the global path planning problem of the mobile robot, the paper uses the immune algorithm. The paper references biological immune mechanism, then designs a route search algorithm, and it is immune algorithm. The algorithm takes advantage of the clonal selection theory, and also introduces vaccination, antibody concentration and other thoughts. Vaccination can improve the affinity, and it also can speed up the convergence. Antibody concentration maintains the diversity of antibodies, so it can prevent local convergence. The paper uses Matlab to do simulation experiments, and make comparison with the genetic algorithm. The results show that the immune algorithm has a stronger capability in global search and convergence faster.Secondly, the paper uses the improved Ant Colony Algorithm to study the global path planning problem of the mobile robot. In order to improve the algorithm's global search ability and convergence speed, a new ant colony algorithm is proposed. The algorithm proposes three improved strategies based on the basic ant colony algorithm:target heuristic strategy, updating and defining pheromone strategy, fallback strategy. Experimental results show that the improved ant colony algorithm has better planning results than the basic ant colony algorithm. This paper also analyzes the main parameters of the ant colony algorithm, and get the best combination of each parameter by the method of simulation experiments.Both of the immune algorithm and the ant colony algorithm have shortages, the paper also combines the immune algorithm with the improved ant colony algorithm, and design a new algorithm, called the immune ant colony algorithm(IACA). Firstly, the immune algorithm has a strong ability in global search and the IACA can use the immune algorithm to get some solutions. Then the IACA generate the initial pheromone distribution based on the solutions. Finally, the IACA use the improved ant colony algorithm to search for the optimal path. Simulation results show that the IACA has a better capability in path search and convergences faster.Finally, the paper also does a preliminary study on the local path planning of mobile robot. The paper mainly learns the principles of the rolling window. Aiming at the local problems of the dynamic obstacles, a partial collision avoidance is proposed. Aiming at the problems of the temporary static obstacles, using a the improved ant colony algorithm in a rolling window is proposed. Simulation results show that the local collision avoidance strategies and the second path planning method are feasible.