Research On Path Planning Algorithm In Two-dimensional Static Environment

There are many research on mobile robots,which are always a hot issue in the field of control,and path planning algorithm is also very important for mobile robots.This paper mainly studies the path planning of mobile robot in two-dimensional static environment.Aiming at the defects of improving artificial potential field method and RRT algorithm,three optimization algorithms are proposed in this paper.In this paper,the problem of local minima is discussed when the artificial potential field method faces large concave obstacles.A * algorithm is introduced to combine it with the artificial potential field method.According to the specific switching mode,the two methods are switched to meet different path States,so as to solve the problem of local minima oscillation of mobile robot and improve the path planning It reduces the time of path planning and shortens the path distance.In addition,this paper also improves the fast search random tree algorithm.Aiming at the problems of too much randomness and long path for RRT,this paper puts forward the idea of slime mold biological model.Based on the traditional fast search random tree algorithm,the idea of biological model is added,which speeds up the convergence speed of the algorithm,reduces the randomness of the algorithm and shortens the path distance.This paper also studies the asymptotic optimality of RRT,and proposes a path reconstruction RRT optimization algorithm based on multiple planning.The path planning problem is regarded as an information filtering problem,and the data is processed after Bayesian filtering,so as to obtain a better path,optimize the path results,reduce the curvature of the generated path,and increase the smoothness of the path.This paper also analyzes the correctness,completeness and complexity of the above three algorithms,in order to ensure the rationality of the above three algorithms in mathematics,and compares the analysis results with the original algorithm,so as to show that the improved algorithm is better.In addition,for the above three schemes,this paper uses MATLAB software to simulate the path,and compares the simulation data with the original algorithm and the improved algorithm proposed by other researchers.The experimental results show that the three algorithms proposed in this paper are effective and stable.Experimental results show that the algorithm in this paper is effective in reducing computing time,improving computing efficiency,shortening the path and improving the quality of the path,which illustrates the value and practicability of the research in this paper.