Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

Abstract

This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesianinference-based OSL method.

Analytical Model of Olfactory Sensory Information in Robot Odor Source Localization

“…The specific expression is that, in the outdoor environment, the flow direction of atmosphere flow field changes in real time, the flow direction and velocity in a certain area is consistent at a particular time period. In the reference [14], Li Jigong has verified the hypothesis and has observed that when wind speed is not less than 0.2 m/s, the distance is not more than 10s of displacement with the speed of the wind, flow field meet approximate uniform hypothesis. So the plume probability distribution within 10s obeys the law of the following formula.…”

“…The third kind is the combination of the first two kinds of methods, of which typical methods include using bayesian inference [13], fuzzy logic and particle filter [14] to estimate the position of the odor source, or using DAPSO algorithm, BFO algorithm, ACO algorithm [15]to survey and map odor concentration field in the process of robots' searching odor source.…”

Using Chemical Sensors as “Noses” for Mobile Robots

“…Most of the gas sensing robots reported so far are equipped with commercial metal-oxide gas sensors because of their reasonably high sensitivity and fair response time (typically <5 s) [17][18][19][20][21][22][23][24][25][26][27][28][29]. They respond to flammable gas at a sub-ppm level.…”

A Graph-Based Formation Algorithm for Odor Plume Tracing

“…Experiments have also been conducted with multiple robots [10,17,13], acting both independently and cooperatively. Other approaches include Braitenberg-type control [16], probabilistic inference [28,17,15] and meta-heuristic optimization methods [1,2,3,12,21].…”