| With the development of intelligent mobile robots,mobile robots are more and more widely used in industry,medicine,service,aerospace and other fields.Path planning is an important subject in the field of mobile robot research.The complexity and variability of the working environment of the robot determine that the performance of path planning is an important index to measure its intelligence level.Path planning of mobile robots refers to the search of an optimal or sub-optimal collision-free path from the starting point to the target point according to the corresponding performance indicators(path length,energy consumption and movement time).In this paper,the path planning of mobile robots is studied as follows:1)Aimed at the shortcomings of the traditional artificial potential field method,such as goal unreachable and local minimum,an improved algorithm is proposed.Firstly,the distance function between the robot and the target is added to the traditional repulsive force field function to make the force zero when the robot reaches the target point and there will be no back-and-forth oscillation problem.Secondly,for the local minimum problem,a fuzzy control algorithm is used to optimize the repulsive force coefficient and dynamically adjust the repulsive force to make the robot escape from the local minimum.In order to make the robot avoid dynamic obstacles,the relative velocity term and acceleration term of the robot and dynamic obstacles are added into the repulsive potential field function.Finally,the simulation results by MATLAB show that the improved artificial potential field method can solve the problem of target inaccessibility and local minimum,and enable the robot to avoid obstacles with known motion information and reach the target successfully.Compared with other path planning algorithms,the results show that the shortest path to improve the artificial potential field method is 14.78 m and the highest accuracy is 98.76% in the environment where the motion information is known.2)Robots may encounter unknown obstacles that suddenly break into the working environment in the course of movement,in order to avoid unknown obstacles in time,this paper combines the improved artificial potential field method with real-time decision-making system,and proposes a local path planning algorithm based on improved artificial potential field method and real-time decision-making.Real-time decision-making system is a hybrid decision-making system consisting of central decision-making and decentralized decision-making.In the real-time decision-making system,a method of predicting the speed and direction of random obstacle motion is established.Then the decision-making system can make the robot avoid unknown obstacles in time according to the movement trend of obstacles.The performance evaluation functions of path length and energy consumption are added to the path planning.Finally,the simulation results by MATLAB show that the mobile robot can avoid unknown obstacles suddenly intruded and reach the target point in time.Compared with other path planning algorithms,the results show that the shortest path planning algorithm based on improved artificial potential field method and real-time decision-making is 14.96 m and the highest accuracy is 96.43% in the environment where the motion information is partly unknown..3)An experimental platform for path planning of mobile robots is built for experimental verification.Firstly,a workspace map model is built in Mapper3,a special map creation software of Pioneer Robots series,and simulated in MobileSim simulation software.The results show that the robot can avoid known and unknown obstacles and reach the target point smoothly.Then,Pioneer3-DX mobile robot is used to carry out experiments in known and unknown obstacle environments.The experimental results show that the robot can avoid known and unknown obstacles and reach the target point smoothly.The experimental results verify the feasibility and effectiveness of the improved algorithm. |
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