Adaptive Lévy Taxis for odor source localization in realistic environmental conditions

Abstract

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three mothinspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate.

A 3-D bio-inspired odor source localization and its validation in realistic environmental conditions

“…Even though it is a crucial parameter of the algorithm, in the literature, this value is usually set empirically and kept fixed for multiple runs, during which the shape and other characteristics of the plume might change. In order to systematically determine the appropriate value for each run, we have developed a mathematical approach based on the Advective-Diffusive Equations (ADE) presented in [14]. Using a data set of samples taken before each run along the crosswind section of the experimental environment, the cumulative distribution function is established.…”

“…In [12] and [13] Lochmatter presented a novel mothinspired algorithm called Surge-Cast, tested along with two other algorithms of the same class, namely Casting and SurgeSpiral, with one wheeled robot. In our previous work [14], we adapted a Lévy Taxis algorithm to the plume tracking phase, by adjusting the key parameters of the algorithm during the run, using the odor concentration gradient sensed by the robot in the environment. The resulting method turned out to be more robust to a larger variety of environmental conditions when compared to typical bio-inspired algorithms such as Casting, Surge Cast, Surge Spiral.…”

Gas source localization and mapping with mobile robots: A review

“…Based on the 2D spiral‐surge algorithm (Ferri et al, 2009), which performs straight line surges in the presence of the plume, and spiral casting once contact with the plume is lost, the 3D adaptation instead performs spiral casting on the Y – Z plane. In order for the algorithm to work successfully, a suitable gas concentration threshold was determined through using an approach based on advection‐diffusion equations (Emery et al, 2017). This ensured that an optimum value was chosen based on environmental conditions.…”

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

“…The plume tracking algorithm in this paper is a modified version of Lévy Taxis, which was originally a random walk-based plume finding method proposed by Pasternak et al [46]. The Lévy Taxis algorithm was modified as Adaptive Lévy Taxis [47] and Fuzzy Lévy Taxis [9] to work as plume tracking algorithms. With the Lévy Taxis plume tracking algorithm, as soon as the robot starts its odor plume tracking task from a random position in the searching area, it conducts random walk behaviors: at each step, the robot turns its heading to the angle and moves forward for a length .…”

“…In order to determine the key parameters , , and in the Lévy Taxis controller, Adaptive Lévy Taxis [47] formulated the parameters as fixed functions of the concentration gradient . and are the odor concentration values measured in the current step and the previous step, respectively.…”