Robot Autonomous Exploration Mapping Based on FD-RRT

In order to enhance the autonomous exploration efficiency of robots in unknown environments, this study proposes a Fast Double Rapidly Exploring Random Tree (FD-RRT) algorithm. The algorithm combines a globally exploring random tree with adaptive step size and a locally exploring random tree with directional bias, aiming to quickly locate the boundary points of the map. In the algorithm design, considerations encompass information gain of the map, redundancy of information in boundary points, and navigation cost of the robot. Simultaneously, by eliminating the redundant information in boundary points and based on the gain in boundary information and exploration cost, different cost values are assigned to navigation points, guiding the robot to prioritize the exploration of regions with lower costs. The effectiveness of the proposed strategy is validated through a series of simulation experiments and real-world applications.