We investigate ant-colony-inspired foraging strategies for  
enhancing the efficiency of a swarm of artificial agents engaged  
in a search-and-retrieval application. First, we extend a mathematical  
model of ant foraging to account for the evolution of  
the information collected during search-and-retrieval over time.  
We then use the extended model to numerically investigate the  
efficiency of search-and-retrieval under the two distinct cases  
of non-depleting information and depleting information at the  
sources. In the former case, we obtain optimal ranges of parameter  
values of the ant foraging model that enhance efficiency.  
In the latter case, we find that appropriately designed deposition  
functions in the model can induce self-organization in the swarm  
and therefore prioritize the collection of quickly depleting information.  
The ability to prioritize is highly desirable in a swarm for  
search-and-retrieval applications and, to our knowledge, induced  
emergent behavior resulting in prioritization capabilities has not  
been reported in swarms inspired by ant foraging. The results are  
expected to be broadly significant for swarm robotics as well as  
in applications such as the Travelling Salesman Problem (TSP)  
with time-varying profit.


Prioritized Foraging Strategies for an Ant Colony-Inspired Swarm System

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

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