Research On Path Planning Of Intelligent Mobile Robot In Warehouse Environment

As an important part of logistics,warehousing operation efficiency has been concerned.At present,many logistics warehouses have used intelligent mobile robots to carry goods,which improves the work efficiency.As one of the important technologies of autonomous navigation for mobile robot,path planning determines whether the mobile robot can reach the designated target point efficiently and safely.Path planning is divided into global path planning and local path planning according to different working states and environmental information.As the storage environment is dynamic and complex,a single path planning method can not meet the needs of work,so this paper combines the two methods to design a hybrid path planning method.The main research contents are as follows:First,when mobile robot uses traditional ant colony algorithm for global path planning,there are some problems,such as low search efficiency and easy to fall into the local optimal solution.Therefore,an improved immune ant colony hybrid algorithm is proposed.Firstly,the immune algorithm has fast global convergence,and the optimal solution is obtained as the initial pheromone distribution of ant colony algorithm.On this basis,the improved ant colony algorithm is used for global path planning,which effectively solves the problem of low efficiency due to the lack of pheromone in the early search.Finally,MATLAB software is used for simulation experiment.The experimental results show that the improved immune ant colony algorithm can better solve the path planning problem of mobile robot in complex environment,and has good robustness and feasibility.Second,the complexity and dynamic degree of the current logistics storage environment are getting higher and higher,which requires mobile robots to avoid dynamic obstacles in time.Traditional artificial potential field method has some problems in local path planning,such as target unreachable,poor obstacle avoidance ability,easy to fall into local minimum and so on.Aiming at many problems of traditional artificial potential field method,a fuzzy artificial potential field method is designed.By setting the distance threshold to improve the traditional gravitational potential field function,introducing the distance between the mobile robot and the obstacle to improve the traditional repulsive potential field function,and then introducing the relative velocity between the mobile robot and the moving target point and the relative velocity between the mobile robot and the dynamic obstacle,a new improved artificial potential field method is obtained.The improved artificial potential field method is combined with the fuzzy logic control algorithm to fuzzify the coefficients in each function,which solves the defects of the traditional artificial potential field method.Finally,the local path planning simulation experiment is carried out on MATLAB.The simulation results show that the fuzzy artificial potential field method can make the mobile robot avoid the dynamic obstacles and reach the target point smoothly,which has good feasibility.Thirdly,the hybrid path planning experiment of mobile robot is carried out in real environment.Firstly,the development environment and ROS system of mobile robot are introduced.Secondly,the components of the hardware platform of mobile robot are introduced,and the kinematic model of mobile robot is analyzed.Finally,the path planning experiment in the actual environment is carried out.Before the experiment,the position of the obstacles is arranged,and the lidar loaded by the intelligent mobile robot is used to collect the actual environmental information,and the collected information is uploaded to the upper computer.After processing by SLAM algorithm,the environmental map is constructed.The mobile robot uses the improved immune ant colony algorithm for global path planning,designs an optimal path,and then uses the fuzzy artificial potential field method for local obstacle avoidance behavior in the driving process,and finally reaches the designated location successfully.The experimental results of mobile robot path planning in real environment prove that the hybrid path planning algorithm is applicable and feasible.