Mobile Robot Path Planning Method Based On Finite Element Map

Nowadays,robots are playing an increasingly important role in our daily lives and have been widely used in all areas of production and life.Therefore,intelligent mobile robots have received extensive attention from various industries in various countries around the world.As one of the core problems of intelligent mobile robots,the path planning algorithm is naturally the hot research direction of various universities.It guarantees that the robot can successfully complete a given task in a complex working environment.According to the various problems in the use of the random sampling path planning method,the following work is done in this paper:1.Aiming at the problem of the poor quality and more turning points of path generated by the traditional RRT algorithm in the actual application process,this paper proposes a DP-B spline mobile robot path smoothing algorithm based on the RRT algorithm.For the path nodes generated by the RRT algorithm,the algorithm first uses the Douglas-Peucker algorithm to extract waypoints,removes redundant nodes,and reduces the number of path transitions;then uses the B-spline function to fit the waypoints to get a smooth path.The simulation experiments in different environment maps verify the effectiveness of the algorithm2.In order to solve the problems that the traditional PRM algorithm have non-unique paths when performing path planning tasks and have low efficiency in an environment map with many narrow channels,this paper proposes a path planning algorithm for mobile robots based on finite element maps.This algorithm innovatively uses the finite element method to triangulate the robot's environment map to generate a weighted undirected cycle graph.Then based on the weighted undirected cycle graph,a Dijkstra search algorithm is used to find a feasible path Finally,this algorithm borrows the idea of the DP-B spline path smoothing algorithm to do secondary processing for the planned path,firstly uses Douglas-Peucker algorithm to extract the waypoints in the planned path,and then uses the cubic spline function to fit the path to get a smooth path curve.The feasibility and effectiveness of the algorithm are verified by comparison experiments with the PRM algorithm and experiments in a complex environment map.At the end of the article,we have summarized the full text,explained the main innovations and issued that require further research in the future.Figure[37]table[12]reference[66]...