Abstract
In this paper we develop Active SLAM-based on-line planning systems for mobile robot olfaction. This On-line planning system enables mobile robot olfaction to perform mapping tasks autonomously when a disaster occurs involving Chemical, Biological, Radiological or Nuclear (CBRN) materials. In this work the robot will be tasked with mapping gas distribution in contaminated unknown outdoor environments. Global and local planning is a major part of the on-line planning system. Global planning serves to provide global path planning and predict the best location to take measurements. The combination of frontier based exploration and Closest Location-Information Gain (CL-IG) methods is used to build global planning. Local planning controls robot navigation to avoid obstacles and evaluate every robot action when performing area coverage and gas distribution mapping. Local planning was developed using sensor path planning based on Bayesian Adaptive Exploration (BAE) and Vector Field Histograms (VFH) methods for obstacle avoidance. Online planning performance testing for robot navigation when exploring and mapping has been simulated using ROS, Gazebo and Rviz.
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