In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.

This paper addresses the problem of estimating the odor path which is most likely taken by the odor patch detected by the concentration sensor on a mobile robot moving in an indoor dynamic airflow environment. The odor path estimation is useful for plume tracing and odor source declaration. A novel algorithm for odor path likelihood mapping in the dynamic airflow environment is proposed. The algorithm has a low computation cost by importing the idea of dynamic window approach. Experiments are carried out on the mobile robot in which odor concentration sensor, airflow sensor, encoder and compass are equipped. To extract useable concentration information from the odor sensor, a practicable data preprocessing method is put forward. The experiment results in the indoor dynamic airflow environment show that the odor path can be well estimated online.

The odor source localization using a mobile robot in time-variant airflow-field environment is considered. A novel estimation-based plume tracing method is proposed in this paper. Based on the odor-patch path estimated by the flow information when odor clues are found, a searching route is planned for the mobile robot to trace the plume. To have fair and statistic comparisons with both the spiral-surge and zig-zagging plume tracing algorithms, a simulation experiment platform is built and two evaluation criterions, i.e., the approaching index to the source and the mechanical efficiency, are proposed. Experiment results in large-scale time-variant airflow field indicate that estimation-based plume tracing method behaves better.

This paper focuses on the problem of mapping odour sources using a mobile robot in a time-variant airflow environment, and provides a localization method which uses the Dempster-Shafer (D-S) theory to reason the possible locations of odour sources. In the proposed method, the robot carries out the D-S inference and iteratively updates a grid map, using the successive measurements from a gas sensor and an anemometer when the robot is cruising in the given search area. Simulations are carried out and the results in a time-variant airflow environment show that the locations of the multiple odour sources can be estimated online with the proposed method.