Path Planning And Application Of Mobile Robot Based On Swarm Intelligence

The application scope of mobile robot’s autonomous navigation ability is more and more extensive under the current science and technology,so a good path planning ability is particularly important.It can make the mobile robot plan a smooth and robust path more quickly in its environment,so as to ensure that it can reach the destination and complete the task safely and stably.The main research content of this paper is the optimization and improvement of fireworks algorithm and ant colony algorithm.Aiming at the shortcomings of the two algorithms,we improve and integrate them to obtain better performance,and verify the effect of optimization and improvement through experiments.The main research contents are as follows:(1)Aiming at the problem that the traditional fireworks algorithm can not find the optimal path because of its long running time and serious memory occupation in path planning,in order to improve the running speed of fireworks algorithm and obtain a shorter path length,this paper proposes a method to optimize and improve fireworks algorithm.Firstly,the connection rules of fireworks algorithm explosion sparks are modified,then the explosion sparks and mutation operator are combined,and the redundancy of spark nodes is optimized.Finally,the selection rules of fireworks population are improved and optimized.Through comparative simulation experiments,it is verified that the planning efficiency of optimized fireworks algorithm is improved,the amount of computation is reduced,and the optimization ability is improved.(2)In view of the problem that the traditional ant colony algorithm is easy to fall into the local optimal solution and can not find the optimal path,a certain number of pheromone enhancement points are set up by using the explosion of fireworks algorithm,which increases the difference of path nodes in the early stage and speeds up the iteration speed in the first and middle stages.The way of segmented path planning is used to avoid the overlapping of the functions of different pheromone strengthening points.Secondly,the redundant path nodes are optimized through a certain number of segmented quadratic planning.In the case of two mobile robots,in order to carry out collaborative planning and reach different destinations,a variety of group planning mechanisms are used.The dynamic obstacle avoidance strategy is proposed through the information sharing mechanism to help the mobile robot avoid the dynamic obstacles in the map.The simulation results show that the improved method has stronger optimization performance and fast convergence speed.(3)Experimental verification of the improved ant colony algorithm:connect two mobile robots through ROS system and SSH protocol,configure a series of parameters for the mobile robot,use the lidar scanning experimental environment of the mobile robot to establish a two-dimensional map,and configure the map into the mobile robot.The positioning module communicates with the mobile robot to ensure that the mobile robot can locate its own coordinates more accurately.After starting the mobile robot,the mobile robot finally avoided the moving obstacles and reached the end point according to the improved ant colony algorithm,which confirmed the effectiveness of the algorithm.