We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surge-spiral and surge-cast) in laminar wind flow. Based on the geometry of the trajectories and the wind direction sensor error, we calculate the distribution of the distance overhead and the mean success rate using Bayes inference. Our approach is related to particle filtering and produces smooth output distributions. The results are compared to existing real-robot and simulation results, and a good match is observed.
Looking for publications? You might want to consider searching on the EPFL Infoscience site which provides advanced publication search capabilities.
We introduce a novel bio-inspired odor source localization algorithm (surge- cast) for environments with a main wind ﬂow and compare it to two well-known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar ﬂow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimental results and some theoretical considerations are provided as well. We conclude that the surge-cast algorithm yields signiﬁcantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm.