Global Path Planning Of Mobile Robot Based On Improved Theta* Algorithm

Path planning is an important part of autonomous navigation technology for mobile robots.Its task is to find an optimal collision-free path from the starting point to the target point in the robot working environment according to specific requirements or evaluation indexes.Grid method is currently the most commonly used environmental modeling method,which can simply and accurately represent environmental information.Based on the grid map,the Theta* algorithm,as a heuristic direct search algorithm,has the advantages of better solution quality and high stability,but the search efficiency is not high enough and the path quality can be further improved.This paper takes Theta* algorithm as the research object,analyzes and optimizes the standard algorithm and the existing improved algorithms,and finally proposes the bidirectional Theta* algorithm integrating environmental information.The main content,technical methods and conclusions are as follows:(1)The standard Theta*,Lazy Theta* and other algorithms are briefly introduced,and some of their deficiencies and limitations are analyzed.For example,there may be a small number of invalid turning points in the planned path,which is not the shortest path;without sacrificing the quality of the path,the search efficiency is not ideal.(2)From the perspective of map scale,the algorithm is improved and optimized,and the bidirectional Theta* algorithm is proposed.Removing the hierarchical restriction of the node during the visibility check to solve the invalid turning problem in the path.Using the strategy of searching to each other from the starting point and the target point at the same time to reduce the search range.Experimental results show that compared with the standard algorithm and existing improved algorithms,the proposed bidirectional Theta* algorithm has a shorter path and higher search efficiency,and can adapt to grid environments of different scales.(3)From the perspective of environmental complexity,the algorithm is improved and optimized,and the bidirectional Theta* algorithm integrating environmental information is further proposed.Using an improved heuristic function,which comprehensively considers the Euclidean distance between the current node and the target point and the complexity of the local environment to enhance the search-oriented ability.Using the method of map preprocessing combined with the integrated image algorithm and connected domain analysis commonly used in the field of image processing,the rapid calculation of the complexity of the local environment is realized.Experimental results show that compared with the other algorithms mentioned above,the proposed bidirectional Theta* algorithm integrating environmental information has higher search efficiency and the shortest path,can adapt to grid environments of different complexity,and has better robustness.In addition,aiming at the turning problem of the planned path,the path smoothing method is proposed to improve the smoothness of the path.Through simulation experiments and processing and analysis of experimental results,the effectiveness and superiority of the proposed algorithm have been fully verified,but limited to current conditions,it has not been implemented in physical experiments,and related work needs to be fulfilled in the future.