Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications

In this paper, a novel approach for fine-tuning of the grid-based map-building algorithm is reported. The traditional occupancy grid-based map-building algorithm uses a fixed probability distribution function of the sonar readings and disregards the information from the environment. In our approach, the probability distribution function is tuned by fuzzy rules formed from the information obtained from the environment at each sonar data scan. A Bayesian update rule is then used to update the occupancy probabilities of the grid cells. The proposed map-building algorithm is compared with other grid-based map-building methods through simulations and experiments. The simulation and experimental studies suggest that ‘sharp’ grid maps can be obtained by incorporating fuzzy rules during the grid-based map generation. In comparison with other algorithms, improved convergence has also been noted.