The Impact of Obstacles on Data Exchange in a Multiagent Decentralized Robotic System

When using autonomous robots for group foraging, it is extremely important to properly organize the exchange of information between members. In a decentralized system, it can be accomplished through pairwise interactions between closely located agents. The paper examines the role of obstacles and bottlenecks in creating an environment conducive to multiple exchanges. Reinforcement learning of groups with various exchange process organizations and obstacle distributions showed a weak impact on the results of randomly placed obstacles and a more significant effect of extended obstacles for both simulated and real robots. The role of data sharing turned out to be higher at the initial stage of the system’s operation and in changing operating environments. The results of the work can be used to organize data exchange when training groups of agents.