Intelligent RRT Exploration Mapping Method Based on Evolutionary Cognition in Unknown Environment

Abstract

How to make robots explore effectively in unknown environments is one of the key issues in robot navigation. Among them, the exploration of efficiency and traversal issues is the embodiment of effectiveness. To solve this problem, an Intelligent Rapidly-exploration Random Tree (IRRT) algorithm based on evolutionary cognition is proposed. This algorithm can intelligently adjust the growth rate of the global tree according to the complete situation of the exploration map. Using the method of simultaneous generation of global tree and local tree to explore boundary points, the detected boundary points are filtered by the DBSCAN clustering method to obtain the target point set, which is used as the current target point of the robot movement. Simulation verification in the Robot Operating System (ROS) shows that the algorithm can complete the traversal of the unknown environment, and effectively reduce the time cost.