Multi-Agent Foraging: state-of-the-art and research challenges

Background

The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.

Results

First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems

Conclusions

Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic.

 

Multi-Agent Foraging: state-of-the-art and research challenges
Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi and Giancarlo Fortino
Complex Adaptive Systems Modeling 2017 5:3
DOI: 10.1186/s40294-016-0041-8

Source: casmodeling.springeropen.com