Braitenberg swarm vehicles for odour plume tracking in laminar airflow

Abstract

This paper presents an algorithm to trace an odour plume using swarm robots in laminar airflow. The algorithm proposed here aims to bridge the gap between single and multiple element systems by mimicking and enhancing biologically derived strategies for odor plume tracking. Simulations were carried out on Webots to verify the potential of the algorithm. A simple gas sensor model was introduced to mimic the response of a real metal oxide sensor in the simulation. A gas sensor model was introduced based on the response of metal oxide sensor (MOS) to closely mimic and provide real environment condition. Different weightage configurations of the gas sensor, k g and wind sensor, k w are compared to find its effects on the performance and behavior of the purposed algorithm. It was found that robots separated from the swarm can still perform the plume tracking task. Also, multiple entity systems show an increase in performance compared to single entity robots.

Gas source localization and mapping with mobile robots: A review

“…Referred to as anemotaxis, common examples include the dung beetle inspired algorithm (Russell et al, 2003) and lobster-inspired algorithm (Grasso & Atema, 2002;Grasso et al, 1998;Maes et al, 1996), both of which are dependent on a consistent chemical gradient to achieve successful results. Their operation being similar to that of Braitenberg style chemotaxis which guide robots through moving in the direction of highest concentration Mamduh et al, 2013;Takei et al, 2014). One of the most popular anemotaxis example is that of the silkworm moth inspired algorithm (Kuwana et al, 1996;Rutkowski et al, 2004).…”

Multi-agent collaborative infotaxis search based on cognition difference

“…It results in sporadic and intermittent sensory measurements without gradient pointing towards the source [4]. In such turbulent situations, the gradient-based strategies such as extremum seeking [5], E.coli algorithms [1], and Braitenberg algorithms [6], become ineffective or even invalid while the searcher has to rely on intermittent cues rather than continuous concentration.…”

Development of a Scalable Testbed for Mobile Olfaction Verification

“…Furthermore, real world experiment runs also tend to consume a lot of time—each run takes at least 10 min [ 24 , 25 , 26 , 27 , 28 ]. Not only the experiments themselves take considerable times; post-run conditioning in between experiment runs (resetting gas source and clearing up the air in test environment) requires a long wait [ 6 ].…”