Odor sources mapping with D-S inference in time variant airflow environments via a mobile robot

This paper addresses the problem of localization of multiple odor sources using a mobile robot in a time-variant airflow environment, and provides a mapping method which uses the Dempster-Shafer (D-S) theory to reason the possible locations of several odor sources. In the proposed method, the robot carries out the D-S inference and iteratively updates a grid map in which each cell has two states (i.e., occupied by an odor source, and not occupied by odor source), using the successive measurements from a gas sensor and an anemometer when the robot is cruising in the given search area. Considering the fact that in outdoor environments air almost always flow and vary with time, estimated air-mass paths are used to construct the belief mass functions of the D-S inference. Here the air-mass path is defined as the trajectory most likely taken by an air mass encountered with the mobile robot in a time-varying airflow environment, and an air mass can contain odor molecules and bring them to the robot, or not contain odor molecule and just pass by the robot. Simulations are carried out and the results in a time-variant airflow environment show that the locations of the multiple odor sources can be mapped online with the proposed method.