Abstract
In this paper, we present the development of a low-cost multi-agent system experimental platform for teaching, and research purposes. The platform consists of train-like autonomous agents equipped with local speed estimation, distance sensing to their nearest predecessor, and wireless communications with other agents and a central coordinator. The individual agents can be used for simple PID experiments in a classroom or laboratory setting, while a collection of agents are capable of performing decentralized platooning with cooperative adaptive cruise control in a variety of settings, the latter being the main goal of the platform. The agents are built from low cost components and programmed with open source software, enabling teaching experiences and experimental work with a larger number of agents that would otherwise be possible with other existing solutions. Additionally, we illustrate with experimental results some of the teaching activities that the platform is capable of performing.
Highlights
Advances on control systems and wireless communications technologies have facilitated the implementation of complex multi-agent systems (MAS) applications
Industrial processes, transportation systems, and energy systems, to name a few examples, are among the main areas of impact that can be potentially optimized through the design of control algorithms for systems comprised of several dynamical agents, aiming to work in a coordinated fashion [1,2,3,4,5,6,7]
A given control system that exhibits an acceptable behavior for a MAS composed of a few agents could cause poor performance for a MAS with a larger number of agents
Summary
Advances on control systems and wireless communications technologies have facilitated the implementation of complex multi-agent systems (MAS) applications. Given the nature of these type of systems, whose complexity increases with the number of members and interactions, the study of their coordinated behavior is a challenging task, as is the design of appropriate control systems and their implementation. This motivates the development of experimental setups suitable to study such systems and their most relevant aspects, where scalability issues rank as one of the most important. In this context, scalability refers to the behavior of the MAS when the number of agents increases. A given control system that exhibits an acceptable behavior for a MAS composed of a few agents could cause poor performance for a MAS with a larger number of agents
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