Research On Service Robot Path Planning Based On Improved Ant Colony And Artificial Potential Field Method

The navigation tasks of the service robot mainly include environment map construction,autonomous positioning,path planning and dynamic obstacle avoidance,etc.Path planning is the most important function in navigation technology.However,a single path planning algorithm can only meet the path planning needs of the service robot in a certain environment.In this paper,the hybrid path planning technology under the complex environment of service robot is studied and discussed.The main work contents are as follows:It is difficult for ant colony algorithm to determine the optimal combination of important parameters.In the map modeled for the service hall environment,it is verified that the important parameters of ant colony algorithm have great influence on the optimization quality of the algorithm.And the range of algorithm parameters that can give full play to the best performance of ant colony algorithm in the service hall environment map is obtained.Respectively,the number of free grids is about 6 times of the number of ants,??[1,2],??[5,9],??(0.2,0.8).The problems of particle swarm optimization and ant colony optimization are analyzed.When the ant colony algorithm is used to solve the global path planning of service robot,the planning efficiency is low and the change of algorithm parameters has a great influence on the planning effect.An improved ant colony optimization algorithm based on particle swarm optimization is proposed.Aiming at the time-consuming problem of the improved algorithm,the dynamic inertia weight adjustment strategy of the particle swarm optimization(pso)algorithm and the improved pheromone update strategy of the ant colony algorithm(aco)are proposed to ensure the solution quality and improve the optimization efficiency.Simulation results show that the algorithm can give full play to the best performance of ant colony algorithm.And the planned path was shortened by about 12%,the number of turning points was reduced by about 50%.It can improve the speed of mobile robot to reach the target point and reduce the loss of the robot in the process of movement.The objective unreachable problem and local minimum problem exist in the traditional artificial potential field method for local path planning.By introducing both the target distance correlation function and the velocity repulsion potential function of the dynamic obstacle into the repulsion potential field of the traditional artificial potential field method,the improved artificial potential field method can avoid the problem of the unreachable target in local path planning and avoid moving obstacles.Simulation results show that the improved algorithm is effective.A hybrid path planning method based on improved ant colony and artificial potential field is proposed for the path planning of service robot in this paper.The kinematic modeling of Dashgo D1 mobile robot experiment platform is carried out.The experimental environment of path planning was built in the indoor environment,and the gmapping SLAM algorithm was used for the map construction experiment.By adding dynamic obstacles into the static map of the hybrid path planning experiment,it is proved that the hybrid path planning algorithm in this paper can make the mobile robot moving along the global optimal path while avoiding temporary dynamic obstacles.