Swarm robotic plume tracking for intermittent and time-variant odor dispersion

Abstract

This paper presents a method for odor plume tracking by a swarm of robots in realistic conditions. In real world environments, the chemical concentration within an odor plume is patchy, intermittent and time-variant. This study shows that swarm robots can cooperatively track the odor plume towards its source by establishing a cohesive spatial sensor network to deal with the turbulences and patchy nature of odor plumes. The robots move together and maintain a distance margin between themselves in order to keep the cohesion of the constructed sensor network while the odor concentration and air-flow speed are considered in the equations of navigation of the robots in the network to more efficiently track the plume. The method is evaluated in simulation against various number of robots, the emission rate of the odor source, the number of obstacles in the environment and the size of the testing environment. The emergent behavior of the swarm proves the functionality, robustness and scalability of the system in different conditions.

Optimal Swarm Formation for Odor Plume Finding

“…Plume tracking is not in the scope of this paper, however, a robot which gets into the odor plume can perform another behavior to inform the other robots to get into the plume and track it. The swarm can cooperatively track the odor plume towards its source (using a method like [15]) and ultimately localize the source.…”

“…By adding (or removing) robots to these scenarios the configuration of forces will change, however, these figures only show the virtual forces in the current setup of the figures before adding another robot. These forces are obtained by implementing the equations (12) to (15).…”

“…Most of the works concerning olfactory search have focused on odor plume tracking [15]- [20] and mapping [21]- [23], whereas plume finding has received little attention. Bio-inspired [16], [17], concentration gradient climbing (chemotaxis) and up-wind directed search (anemotaxis [19], [24], [25]) are the most common approaches to track odor plumes by mobile robots.…”

“…Most of these studies (e.g. [15]- [18], [32]) assume that the robots start their search within or very near the plume.…”

“…Bio-inspired [16], [17], concentration gradient climbing (chemotaxis) and up-wind directed search (anemotaxis [19], [24], [25]) are the most common approaches to track odor plumes by mobile robots. Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approaches (BESA) [26], biased random walk (BRW), evolutionary strategies [27], particle swarm optimization (PSO) [28]- [30], glowworm swarm optimization (GSO) [31], gradient climbing techniques, swarm spiral surge [32], physics-based swarming approach [33], and attraction/repulsion forces [15]. Most of these studies (e.g.…”