Study On Hybrid Path Planning Method For Indoor Mobile Robot

With the rapid development of technologies such as sensors and artificial intelligence,mobile robot technology has also been greatly improved.At the same time,in order to meet people's needs for a more intelligent life in the future and to deal with the upcoming population aging problem,countries are also increasing research in the field of mobile robots.In a more complex and changing workspace,how to plan a path that meets multiple performance indicators for indoor mobile robots is a challenging problem.According to whether the acquired environmental information is complete,path planning can be divided into global and local path planning algorithms.However,only one algorithm can not meet the actual needs.Specifically,global path planning cannot avoid obstacles in real time,and local path planning is easy to fall into local extremes.In response to the above problems,this article combines the bat algorithm with the dynamic window method to complete the path planning task in a complex and changing environment.The main research contents are as follows:A global path planning method based on improved bat algorithm is proposed.By using Markov chain to analyze the convergence of bat algorithm,the influence of adjusting inertia weight on the convergence accuracy and convergence speed is clarified.The global search ability and local search accuracy of the algorithm are improved by using the logarithmic decline strategy to dynamically adjust the inertia weight.The global optimal solution is added to the Cauchy disturbance as a factor to improve the utilization ability of the new local solution formula to the global optimal solution.The performance of the improved algorithm is tested by eight classical test functions,which verifies that the improved algorithm has better convergence and convergence accuracy.The length and smoothness of the path are taken as the evaluation indexes,and the improved algorithm is used for global path optimization to verify the optimization ability of the algorithm.An improved dynamic window method is designed as a local path planning method.Aiming at the problem of unreasonable path selection in traditional dynamic window method when it is close to the target point and dense obstacles,an improved dynamic window method is proposed.A new objective function is introduced in the algorithm to improve the speed selection when the robot approaches the target point.The number of obstacles is introduced into the velocity evaluation function to improve the problem that dynamic window method is easy to bypass dense obstacles.Based on the above two algorithms,a hybrid path planning algorithm is proposed.The turning points in the global path are successively regarded as the sub-target points of the dynamic window method to guide them to complete the path planning task.A simulation experiment was carried out in Matlab to verify the effectiveness of the hybrid algorithm by adding random static and dynamic obstacles.In order to realize the obstacle avoidance function of multi-robot formation while taking into account the kinematic characteristics of robots,this paper introduces dynamic window approach and behavior method to realize formation avoidance.The formation,switching,and formation of obstacle avoidance functions are verified in simulation experiments.Finally,the proposed formation obstacle avoidance algorithm is combined with global path planning to realize the hybrid path planning of the robot formation.