Research On Path Planning Algorithm For Mobile Robots

With the development of society,mobile robots have been widely used in more and more fields.At the same time,as one of the core contents of robot research,the robot path planning problem has been studied by a large number of scholars at home and abroad,therefore,it is of great practical significance to study the path planning algorithm of mobile robots.The path planning algorithm of mobile robot is divided into two categories: global path planning and local path planning.In this paper,Rapidly-exploring Random Tree(RRT)algorithm in the global path planning algorithm and the artificial potential field algorithm in the local path planning algorithm are deeply studied,and an improved algorithm is proposed.The main research work is as follows:1)Research on Global Path Planning AlgorithmAn Adaptive Target-biased RRT global path planning algorithm is proposed.This algorithm improves the random tree growth method from the perspective of improving the random tree growth method,and aims to grow the random tree towards the target point because the basic RRT algorithm has strong randomness,no search target,and the obtained path is not necessarily the optimal path.Self-adaptive adjustment based on environmental information makes the random tree grow with higher quality.The simulation experiment results show that the algorithm can guide the random tree to grow toward the target point with high quality and the planned path is as close as possible to the optimal path,while shortening the time of path planning.A path optimization algorithm for RRT algorithm is proposed.The algorithm uses the greedy algorithm to optimize the path that the RRT algorithm generates,such as unnaturalness,tremble,and unnecessary path nodes.The path optimization algorithm idea is to choose the current best choice in each step of the optimization process,and then achieve the overall optimization of the optimal.The algorithm is not only suitable for basic RRT algorithm but also applicable to various improved RRT algorithms.Simulation experimental results show that the optimization algorithm can greatly reduce the path planning length and number of path nodes of RRT algorithm.2)Research on Local Path Algorithm and its combination with Global Path Planning AlgorithmA fusion algorithm combining Artificial Potential Field algorithm(local path planning algorithm)and adaptive target-biased RRT algorithm(global path planning algorithm)is proposed.This fusion algorithm aims at the problem that the traditional Artificial Potential Field algorithm is unreachable and easily falls into the problem of local stability.On the basis of improving the Artificial Potential Field algorithm to solve the target unreachable problem,the adaptive target bias RRT algorithm and the improved Artificial Potential Field algorithm are proposed.The simulation results in different environments show that the fusion algorithm can solve the problem that the Artificial Potential Field algorithm is unreachable and can easily fall into the problem of local stability,and realizes the complementary advantages of the two algorithms.