Research On Path Planing And Map Construction Methods Of Mobile Robots

With the rapid development of computer technology and intelligent manufacturing technology,mobile robots have brought a positive impact on human life and work.Mobile robots are a collection of high and new technologies that integrate knowledge in multiple fields such as intelligent systems,applied electronics,computer hardware,sensors,and cover many cutting-edge technologies in the current world.Mobile robots have entered the era of intelligence.For example,the delivery robot designed by the Google team of engineers makes it a reality for robots to replace humans in delivering deliveries.Path planning and map construction are important research contents in the field of mobile robots.Although related intelligent algorithms help mobile robots complete path planning and map construction,there are still problems such as excessively long optimal path length,slow convergence speed,inability to complete path planning in complex environments,and poor map accuracy.In order to solve the above problems,the main research work of this article includes the following four aspects.First of all,this thesis integrates the artificial potential field method and the ant colony algorithm,and gives a mobile robot path planning method that integrates the artificial potential field ant colony algorithm.On the one hand,the influence factor of the distance to the target point is introduced to improve the influence of the potential field force on the path search of the mobile robot.By improving the repulsion field function,the mobile robot is prevented from being subjected to greater repulsion force and unable to plan the optimal path.On the other hand,construct the potential field force heuristic function,taking into account the distance heuristic information and the potential field heuristic information at the same time.The differential allocation of initial pheromone is beneficial to improve the convergence speed of the algorithm.In the later iteration of the algorithm,in order to reduce the interference of pheromone,this thesis introduces the pheromone adjustment coefficient and improves the global pheromone update method,which is beneficial to the mobile robot to quickly search for the optimal path.Secondly,this thesis designs an adaptive variable step length ant colony algorithm,and gives a two-dimensional path planning method for mobile robots based on the adaptive variable step length ant colony algorithm.It solves the problem that the mobile robot is stuck in local convergence and cannot achieve the optimal path in the path planning of the ant colony algorithm,so that it can achieve the optimal path with a small number of convergence iterations.According to the relevant characteristics of ant colony algorithm applied in path planning,the algorithm optimizes pheromone allocation,reduces the influence of local pheromone content on the algorithm,and prevents the ant colony from falling into the local optimum when searching for the path;at the same time,it adds to the transition probability formula The weight factor increases the probability of the mobile robot moving toward the end point,effectively reducing the number of iterations of ant colony convergence;adaptively changing the mobile robot's moving step length so that it can move freely within 360° without collision,effectively shortening the path length.The simulation results show that,in a simple environment,the number of convergence iterations and the optimal path length of the proposed algorithm are better than traditional ant colony algorithm.In complex environments,the number of convergence iterations and the optimal path length of the adaptive variable step size ant colony algorithm are better than the improved potential field ant colony algorithm.The simulation results verify the effectiveness and superiority of the algorithm in this paper.Thirdly,this thesis presents a three-dimensional path planning method for mobile robots based on improved ant colony algorithm.Construct a three-dimensional space model for the three-dimensional map,set the area pointed by the ants from the starting point to the end point as a favorable passing area,increase the initial value of the pheromone in this area,and use the positive feedback effect of the pheromone to encourage the ant colony to reach quickly At the end point,a new diversified heuristic function is designed.The improved ant colony algorithm uses the multiple heuristic function to calculate the probability of reaching the next node in the visible area,and attracts the ants to choose the optimal path through the distance information between the current position of the ant and the end point.Finally,this article is based on the mobile robot built on the ROS platform,which is used for the map construction of the Gmapping algorithm and the Hector-SLAM algorithm.The parameters of the assembled mobile robot are corrected to ensure that the robot can move normally in the configured network environment,and the map construction process is designed to realize the map construction technology based on the Gmapping algorithm and the Hector-SLAM algorithm respectively.In order to verify the actual mapping effect of the two algorithms,this thesis has completed the map construction of the Gmapping algorithm and the Hector-SLAM algorithm in the indoor environment.The experimental results show that the Gmapping algorithm is more suitable for map construction in a small scene environment.