Research On Path Planning Of Autonomous Mobile Robots In Complex Environment

Autonomous mobile robots are widely used in logistics transportation,modern factories,industrial and military specific missions.Path planning is one of the key tasks for mobile robots to achieve autonomous navigation.It is of great value to carry out research on path planning in complex environment.In the path planning of robots in complex environments,especially in dynamic uncertain change scenarios or static multi-objective path planning tasks,intelligent algorithms have low search efficiency,and algorithm optimization research is still needed.Therefore,in order to improve the path planning efficiency and avoid dynamic obstacle of mobile robots in complex environments,the improved firefly optimization methods and algorithms have been studied for the robot's autonomous path planning.The main research contents and innovations are as follows:1.In order to solve the path planning problem of mobile robots in certain complex environments,an adaptive probability function model is proposed to generate the initial path,and the method is applied to the location update of the multi-target firefly algorithm.According to the characteristics of the grid map and the orientation information of the starting point and the target point in the environment,the probability function model of the robot' s moving direction in the grid map is established,and the initial path solution is generated by using the probability function model to improve the efficiency of the algorithm;And path smoothness is also used as the evaluation index of path planning.Combined with initial path generation method,firefly algorithm and Pareto multi-objective solution strategy,the multi-objective non-dominated optimal path solution of mobile robot is obtained.The simulation results show that the initial path solution generated by the probability function model is significantly improved compared with the randomly generated initial path solution.The multiple target evaluation functions of the path planning are compared with the single target optimization.The rotation angle of the path planning task is significantly shortened,which can effectively improve the smoothness of the movement of the mobile robot,and compared with the multi-objective genetic algorithm,the feasibility of multi-target firefly algorithm is verified.2.To solve the path planning problem of mobile robots in uncertain environment,a new dynamic path planning strategy based on firefly algorithm is proposed and the fitness function of firefly algorithm is designed.In order to improve the low efficiency of the intelligent algorithm in the dynamic path planning problem,when there are no obstacles in the safe range of the robot,the intelligent algorithm is not activated,and the robot directly moves to the target point;when the robot detects the obstacle within the safe range,it starts the firefly algorithm performs the obstacle processing.At the same time,a firefly fitness function is constructed that combines the distance from the robot to the obstacle and the distance from the robot to the target point.The simulation results of uncertain path planning in dynamic obstacle scheme show that the proposed method is better than the artificial potential field method and the path length is significantly shortened.3.In order to verify the effectiveness of the firefly algorithm in path planning in reality scenarios,an algorithm test was carried out based on the four-wheel mobile robot system.Firstly,the errors of linear velocity and angular velocity of the mobile robot are corrected,which are caused by the robot hardware in the path navigation.In the path planning experiment of the known environment,the distance and rotation angle of the mobile robot are measured by experiments and compared with the simulation results,the feasibility of the algorithm is verified.In the local dynamic obstacle experiment,the wheeled mobile robot can detect obstacles immediately and avoid them,and find a better path to reach the target point.The test results show the real-time properties and feasibility of the proposed method in path planning.