Research On Path Planning Method Of Mobile Robot In Complex Environment

As robots become more widely used in the real world,how to improve the autonomy of intelligent mobile robots has become a hot research direction in the field of robotics.Path planning is a critical task in intelligent mobile robot control systems,it is of great significance to improve the autonomy of intelligent mobile robots.This paper focuses on path planning problem of intelligent mobile robots,and studies the global path planning and obstacle avoidance technology of mobile robots in complex environments,the specific studies are as follows:Firstly,this paper focuses on the global path planning problem of mobile robots.Considering the shortcomings of the classical rapidly random-exploring trees algorithm,such as poor stability and slow convergence,this paper presents an improved hybrid bidirectional RRT with target gravity algorithm.The algorithm combines the target point search strategy with the random search strategy,the simulation results show that the improved algorithm improves the ability of path searching and reduces the searching time of the algorithm.Subsequently,a new type of swarm intelligence optimization algorithm,fireworks algorithm,is applied to the global optimal path planning of mobile robots.In order to improve the global convergence robustness of FWA algorithm,this paper presents a fireworks algorithm with quantum behavior,through QFWA solving several standard test functions,experiments show that the global search ability and convergence speed of QFWA algorithm are improved.This method is applied to compute the global optimal path problem of mobile robot.Mean filter-artificial potential field smoothing algorithm is used to process the generated path.Experiment in different scenarios,the experimental results show that the mobile robot path planning method based on QFWA algorithm and mean filtering-artificial potential field smoothing algorithm has better optimization performance than the FWA algorithm.Next,the collaborative global path planning problem between multiple mobile robots is studied.The multi-robot collaborative path planning problem is described,and the performance index of multi-robot collaborative path planning is established.Then the FWA algorithm is applied to solve the multi-robot collaborative path planning problem.This paper presents a differential evolution FWA algorithm.Based on the basic FWA algorithm,the differential evolution explosion spark is added,which increases the diversity of the algorithm and makes the explosion spark have better search ability.Through DEFWA algorithm solving several standard test functions,experiments show that the optimization capability and robustness are improved.The DEFWA algorithm is used to optimize the path of multi-robot cooperative.Then,aiming at the dynamic obstacle avoidance of moving obstacles and moving targets for multiple mobile robots in the process of motion,an autonomous obstacle avoidance method for mobile robots based on game theory is proposed.Firstly,the dynamic obstacle avoidance strategy library of robot is established.Then the game profit is calculated according to the relationship between the current state of the robot and the state of other players participating in the game.Through the repeated game process,the player's income reaches the Nash equilibrium,then choose the obstacle avoidance strategy to be adopted according to the game income.The simulation results verify that the strategy update method adopted in this paper can make the participating players converge to the optimal strategy quickly,and can avoid dynamic obstacles.Finally,in order to verify the effectiveness of the path planning and obstacle avoidance algorithm that proposed in this paper,a global path planning and obstacle avoidance system for mobile robots based on ROS is built.Global path planning experiment and obstacle avoidance experiment for mobile robots are carried out in complex environment,experiments verify the effectiveness and usability of the proposed algorithm.