Interior Mobile Robot Path Planning

With the rapid development of disciplines such as artificial intelligence and computing science,mobile robots are increasingly used in agriculture,industry,and service industries.In the process of robots completing tasks,autonomous navigation plays a key role,and path planning technology as an important part of it has attracted the attention of many scientific researchers.However,the known path planning methods have various defects,for instance the Ant Colony Optimization(ACO)is easy to fall into local convergence;the Artificial Potential Field(APF)has the phenomenon that the target point cannot be reached.Therefore,in order to improve the efficiency of path search and optimize the results of the path,this paper improves the Ant Colony algorithm and the Dynamic Window Approach(DWA).Through the path planning framework of the Robot Operating System(ROS),and combining with the A* extended adaptive ant colony algorithm and the improved Dynamic Window Approach,Completed simulation and actual environment verification.The major research substance and achievements of this article include:1.An A* extended adaptive ant colony algorithm to solve the weak point of andante convergence career and investigation efficiency.Firstly,use the A* algorithm to search for the initial path in the grid environment,expand the initial path to construct the advantage area,modify the initial pheromone of the excellent region,and refrain the sightless search of the ACO in the initial stage;secondly,introduce the directionchanging heuristic function and parameters into the transition probability the adaptive pseudo-random proportional rule speeds up the convergence speed;then inferior ant paths are eliminated when the pheromone is updated,and the search efficiency of the algorithm is improved.Finally,simulations verify the feasibility and effectiveness of the A* extended adaptive ant colony algorithm in a variety of environments.2.In view of DWA's defect that excessive heading angle changes cause the robot to move unsteadily,the heading angle offset function is introduced into the DWA evaluation function,which reduces the robot's heading angle change range and makes the robot move faster and more smoothly.In addition,the weight is rewarded and punished based on the deviation angle between the predicted position and the target position,so that the path is smoother.Finally,through multiple sets of simulation comparison experiments,the feasibility of the improved algorithm is verified.3.Build a mobile robot experimental platform,complete the fusion of improved algorithms based on the ROS navigation framework,build a grid map through the Gmapping algorithm,and design a comparison experiment to complete the feasibility verification of the hybrid path planning algorithm.