Frontier-based Exploration on Continuous Radial Basis Function Neural Network Map

摘要

We present a frontier-driven autonomous robotic exploration method on a continuous representation of environment. The approach utilizes radial basis function neural network to build continuous occupancy grid map. Parametric frontiers are calculated directly by gradient field of occupancy probability distribution, which clear show division between free and unexplored space. Besides, the resulting frontiers provide a measure of quality automatically. Simulation is present to show the performance of the proposed technique.