Multi-resolution field-based algorithm for autonomous robot exploration

This paper proposes a robot autonomous exploration algorithm based on multi-resolution fields to solve the problems of low efficiency, high path repetition, and limited environmental adaptability in complex environments. First, OctoMap's layered architecture is used to create a multi-resolution voxel map. A coarse-fine dual stage is then used to detect frontiers and balance point numbers. Second, polar coordinate sampling is utilized to avoid the sampling points being too concentrated in the center of the circle, and the scoring function is constructed by combining the distance decay function to prevent the robot from repeating the search. Finally, Monte Carlo integration's gain calculation method is introduced to eliminate step errors caused by discretization and improve gain calculation accuracy. Simulation experiments are carried out in different environments, and the data prove that the method in this paper is better than the comparison algorithm in terms of moving distance, running time and exploration efficiency. The results show that the algorithm proposed in this paper effectively improves the robot's autonomous exploration performance in unknown complex environments and provides a new solution for practical applications.