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

Since the 21 th century,with the development of the information technology,the research on mobile robot has been received more and more attention.Path planning technology,as a key research object in the field of robot research,ensures the goal of a highly intelligent and safe autonomous movement when the robot autonomously navigates.This technology requires the robot to use various types of sensors to determine its position.It can complete obstacle avoidance tasks from the starting point to the ending point in an environment which containing known or locational obstacles.So far,at home and abroad,there have been further in-depth researches in this field and a certain number of achievements have been achieved.However,due to the random and multi-binding characteristics of robot path planning in different dimensions of work space,the choice of an effective algorithm is still a hot topic.This article first describes the foraging process of ant colony.From the nest to find out the shortest path from the food source is goal.This happens to have a remarkable coincidence with the physical process of robot path planning.The working principle of ant colony algorithm with mathematics is discussed,apply it to path planning.Secondly,due to the ant colony algorithm in the two-dimensional environment,there are problems such as lack of pheromone,blindness,and extended search path length in the path planning.Therefore,an improved ant colony algorithm with strong global search capability and fast convergence speed is proposed.This method introduces directionality,improves the efficiency of route search,uses the self-adaptive adjustment method for the volatility coefficient in the pheromone updating process,and puts forward a new pheromone reward and penalty mechanism.The simulation results show that the improved algorithm is more targeted in selecting the next node and quickly finds the optimal path.In the end,for the complexity of three-dimensional modeling,a node method is used to construct a minimal set of points to build the space.An improved ant colony algorithm method is applicable to three-dimensional environment.The design rules of heuristic function not only consider the shortest path distance as the weighing factor,but also curbside constraint is taken into consideration.In order to improve the direction uncertainty in the initial stage of the search,the value of the initial pheromone is no longer fixed,and in the process of optimization,pheromone updating adopts a combination of global and local methods.The pheromone coefficient is adaptively.The optimization efficiency is improved.The experiments prove the feasibility and effectiveness of the algorithm and can increase the rate.