The Research On Obstacle Avoidance Algorithms For Mobile Robots In Dynamic Scene

Mobile robot path planning is a research hotspot in the field of robotics.Mobile robot path planning refers to the positioning of the robot to its own environment,according to the path planning algorithm to achieve the moving path from the start point to the end point,and at the same time in the process of moving according to the obstacle avoidance module to complete obstacle avoidance and environmental monitoring.This article analyzes and discusses the traditional artificial potential field method,improves the artificial potential field method,and further integrates the RRT*algorithm and the RRT*-Smart algorithm to propose two algorithms:the RRT*-Smart optimization algorithm based on the artificial potential field method,The optimization algorithm combining the artificial potential field method and RRT*solves the local minimum problem and improves the efficiency of path planning.The artificial potential field method establishes a virtual artificial potential field model.Under the joint action of the gravitational potential field and the repulsive potential field,the mobile robot moves toward the low potential energy point in the artificial potential field.The traditional artificial potential field method has two major problems:the target unreachable problem and the local minimum problem.In this paper,the sub-target point is introduced to solve the local minimum problem,and the artificial potential field normal function is improved so that the mobile robot first moves to the sub-target point and then to the final target point to escape from the local minimum point.By studying the basic principles and steps of the sampled rapid expansion random tree algorithm(RRT algorithm),the advantages and disadvantages of the algorithm are analyzed,and then the improved algorithm RRT*algorithm and RRT*-Smart algorithm are introduced.Two fusion algorithms are proposed:RRT*-Smart optimization algorithm based on artificial potential field method,and optimization algorithm combining artificial potential field method and RRT*.The RRT*-Smart optimization algorithm based on the artificial potential field method introduces the idea of RRT*-Smart into the improved artificial potential field method;the optimization algorithm combining the artificial potential field method and RRT*first fuse the artificial potential field method with the RRT*algorithm,Then use RRT*-Smart's path optimization thinking to optimize the entire path,integrate the simplified random sampling process of the RRT*algorithm into the artificial potential field method.The algorithm has the advantages of the high efficiency of the artificial potential field method and the rapid diffusion of the RRT*algorithm.In this paper,the two algorithms were tested in static and dynamic environments.The experiment shows that the RRT*-Smart optimization algorithm based on the artificial potential field method can solve the local minimum of the artificial potential field method.Greatly improve the efficiency of artificial potential field method.The optimization algorithm combining the artificial potential field method and RRT*can escape the local minimum in an unknown complex environment.It combines the characteristics of the artificial potential field method and RRT*algorithm and effectively improves the related defects,and has good applicability.