The Research Of Global Path Planning For Mobile Robot Based On Ant Colony Algorithm

Path planning of robot is a very typical Optimization problem which has many typical characters such as complexity, restriction, non-linear, normative of modeling.Therefore,doingresearch to improve on an intelligent and general-purpose global optimization for large-scaleparallelization is a feasible researching direction.At present,there have been a lot of Optimization methods to solve the problem of path planning of robot,but most of these methods have some limitation.As a novel simulate devolutionary algorithm,Ant Colony Optimization(ACO)has many merits as parallel essence,positive feedback and coordination,so it accords with the tendency that path planning algorithmevolves into intelligent and simulated. In my paper,I have done some research on the problem of path planning of robot based-on improved ACO.What I have done went into details are described as follows:Firstly,this paper introduces the common used planning algorithms for intelligent mobile robot in the country and abroad,and the researching situation is elaborated and summarized,it also analyzing advantages and defaults of each algorithm which made an important basis for the research on mobile robot path planning in the thesis.Secondly,we analysis the essence of ant colony algorithm from the proposing of the algorithm to its the modeling, and this study provides basic theoretical support for our work.Thirdly, through VB6.0 programming,we have done some Valuable work in TSP problem of 48s cities by max-min ACO algorithm.These work provides a solid platform for Path planning of robot, such as the Parameters choice and performance evaluation of the algorithm.At last, programming by the simulation software MATLAB7.0, we analyze the shortecomings of the basic ACO application in Path planning of robot. To overcome these defieieneies, three important improvments have been bought up, experiment suggests that the improved algorithm is effective.