Subsumption architecture based mobile robot gas source localization in time-variant-airflow environment

The subsumption architecture based vision/olfaction fusion method is presented to accomplish the gas source localization task in the time-variant-airflow environments where both the wind speed and direction have relatively large-scale fluctuation. In such environments, it is difficult to describe the distribution of the gas concentration using a mathematic model due to the influence of turbulence. In order to make full use of the multi-sensor information, the different behavior strategies with different priorities are set up. The behavior with higher priority can subsume or inhibit the behavior with lower priority, which makes the robot generate an optimization strategy to deal with the dynamic, complex and unstructured environments. With the subsumption architecture the gas source localization task could be accomplished efficiently. Experimental results show the efficiency of the proposed method.