Research On Path Planning Of Mobile Robot Based On Improved RRT Algorithm

With the development of technology,the application field of mobile robots is increasing day by day,and the importance of path planning technology is becoming more and more prominent as one of the key directions in the research field of mobile robots.This paper focuses on the path planning of mobile robots in static obstacle environments.Based on the Rapid Exploration Random Tree(RRT)algorithm,an improved algorithm is proposed for both simple and complex obstacle environments,aiming to improve the search efficiency of the algorithm and the ability of obstacle avoidance to find paths and generate shorter paths.Its main elements are as follows:Firstly,in the simple obstacle environment,for the problem of large randomness and poor obstacle avoidance when planning paths by the basic RRT algorithm,the gravitational guidance strategy is proposed by drawing on the idea of potential field force of the artificial potential field method,and the gravitational guidance RRT algorithm based on adaptive weights is obtained by giving variable weight coefficients to the random gravitational component and the target gravitational component.The gravitational guidance strategy can pull the random tree to grow toward the location of the target point,which solves the problem of lack of guidance when the RRT algorithm plans the path;the adaptive weights dynamically adjust the expansion direction of the random tree by changing the magnitude of the gravitational component weights,which solves the problem of poor obstacle avoidance ability of the RRT algorithm when encountering obstacles.The improved algorithm effectively improves the directivity and obstacle avoidance flexibility of random tree expansion,reduces the path planning time by 45% and the path length by 20%,improves the planning efficiency and obtains shorter paths.Secondly,in the complex obstacle environment,for the problems that the basic Bi-RRT algorithm is difficult to escape when it is caught in the local extremum region,the nodes in the local region are stacked,and there are many redundant nodes in the path,the extended optimization strategy and node optimization strategy are proposed to obtain the Bi-RRT algorithm based on extended optimization and node optimization.The extended optimization method includes target point selection strategy and collision point offset strategy,which can make the random tree expand faster toward the target point location and improve the ability of the random tree to escape from the local extreme value region;the node optimization method includes local node sparse strategy and path node reconnection strategy,which can solve the problem of node stacking in the restricted environment of the random tree,improve the speed of the random tree to escape from the local extreme value region,and optimize the initial path Optimization is performed to obtain a better path.The improved algorithm has strong environmental adaptability and high planning success rate,especially in zigzag and narrow environments,with a planning success rate close to 100%,an 80% reduction in the number of tree and path nodes,and a 15% reduction in path length.Finally,different obstacle environments are constructed and simulation experiments are conducted in MATLAB using the improved algorithm and the initial algorithm respectively,and the feasibility of the improved algorithm is proved through the analysis and comparison of the simulation data.