Multi And Adaptive Ant Colony System Based On ROS And Its Application And Research

Mobile robot is a significant method to improve productivity and further promote human being's life level;on the other hand,robot path planning is one of the most important technology in mobile robot.Meanwhile,Ant Colony Optimization(ACO)algorithm,promoted firstly in 20 centuries,is one of Intelligent Algorithm which mimics the earth.ACO was used to address TSP at the beginning and has been applied to solve other problems.In this thesis,ACO algorithm has been researched to solve the TSP to understand its theory and then in order to apply it,ACO has been used to cope with mobile robot path planning.To begin with,three traditional and two of the best ACO algorithm versions have been analyzed to handle the principle theories and brilliant ideas of ACO algorithms.In terms of the first ACO edition(Ant System,AS),several important characters from real ant in natural world to abstract mathematical formulas have been summarized,including heuristic information,increase model of pheromone and the relationship between initialized pheromone and pheromone's increasement,as well as stagnation behavior.When EAS and RAS are concerned,diversity,convergence and their relation have been researched except exploration and exploitation of ants.Besides,based on ACS and MMAS,the relation of variation of pheromone and the difference among different paths has been discussed to understand better ideas;further,similarity and distinct between these two algorithms have been discussed and two important measures have been concluded: the best so far road to have a better convergence speed and the limitation towards the pheromone to have obtain a better diversity.Secondly,a new heuristic communication heterogeneous dual population ant colony optimization(HHACO)has been promoted to balance convergence speed and diversity of the solution at big scale travel salesman problem(TSP).The main characteristics of HHACO are heuristic communication and two heterogeneous ant colonies.Heuristic communication between two ant colonies,an indirect communication strategy,accounts for better deviation of solution.Heterogeneous ant colonies accounts for dual ant colonies algorithm having both convergence and deviation,in which one ant colony is in charging of solution deviation and another one ant colony is in charging of convergence inspiring from nature evolution with self-adaptive ability.Then,HHACO algorithm is used to solve TSP,and obtains better experiment results.Besides,with orthogonal test,parameters setting of HHACO have been also discussed and obtain a better parameters combination.Finally,HHACO are compared with other dual colonies algorithms and classic ACO and results suggest that our algorithm have a better performance in big scale problem.Finally,a self-adaption and random ACO based on maximum entropy has been improved to solve mobile robot path planning aiming at improved the heuristic efficiency and avoid its potential drawbacks which tend to make algorithm involve in the local optimum and deficiency to jump the local optimum.A heuristic information based on ratio of equality has been improved to enhance heuristic effect,which makes is have no relation with environment and is good for convergence speed.Also,heuristic data would be used in random situation based on maximum entropy,which help a good diversity at the early stage.In addition,one of grid's heuristic information would be initialized when the algorithm is in stagnation,which can enhance the ability to jump the local optimum and sustain the previous ant's experience.This algorithm has been proved both in MATLAB and in Turtle Bot2.