Chemotaxis Based Virtual Fence for Swarm Robots in Unbounded Environments

Abstract

This paper presents a novel swarm robotics application of chemotaxis behaviour observed in microorganisms. This approach was used to cause exploration robots to return to a work area around the swarm's nest within a boundless environment. We investigate the performance of our algorithm through extensive simulation studies and hardware validation. Results show that the chemotaxis approach is effective for keeping the swarm close to both stationary and moving nests. Performance comparison of these results with the unrealistic case where a boundary wall was used to keep the swarm within a target search area showed that our chemotaxis approach produced competitive results.

Swarm Foraging Under Communication and Vision Uncertainties

“…RepAtt is based on the use of a communication mechanism whose intensity decreases smoothly with increasing distance from the source. The exponentially degrading signal of Equation 1 [34] was used, where A k ij is the strength of signal type k sensed by robot i, located d ij metres away from signal source j. A 0 is the signal strength at the source, while α and A e are the attenuation factor and mean ambient sound levelproperties dependent on environment condition.…”

RepAtt: Achieving Swarm Coordination through Chemotaxis

“…RepAtt is based on the use of a communication mechanism whose intensity decreases smoothly with increasing distance from the source. The exponentially degrading signal of Equation 1 [20] was used, where A k ij is the strength of signal type k sensed by robot i, located d ij metres away from signal source j. A 0 is the signal strength at the source, while α and A e are the attenuation factor and mean ambient sound level -properties dependent on environment condition.…”

“…The parameters of Equation 1 were obtained through experiments using Turtlebot2 hardware platforms, speakers and omnidirectional microphones as described in our previous work [20]. These parameters are: A 0 = 299.18, α = 0.12 and A e = 48.18.…”

A minimal biologically-inspired algorithm for robots foraging energy in uncertain environments

“…Results indicate that due to the emergent pattern future applications may be applied to swarm robotics. Although in this instance they do not communicate or sense each other, it has been noted that in other works swarm robotics may also consist of very minimalist hardware and algorithms, which is interesting in the context of swarm robotics as complex communication devices are also expensive, and emerging patterns can definitely be observed in this algorithm already [11,35]. Furthermore, the work demonstrates a novel application of bio-inspired concepts in the context of the Evolutionary Robotics akin to speciation, using the same algorithm a range of patterns of emergent behaviour appear, similar to the evolution of different species from a common ancestor.…”