Energy-efficient environment mapping via evolutionary algorithm optimized multi-agent localization

Miniature autonomous sensory agents (MASA) can play a profound role in the exploration of hardly accessible unknown environments, thus, impacting many applications such as monitoring of underground infrastructure or exploration for natural resources, e.g. oil and gas, or even human body diagnostic exploration. However, using MASA presents a wide range of challenges due to limitations of the available hardware resources caused by their scaled-down size. Consequently, these agents are kinetically passive, i.e. they cannot be guided through the environment. Furthermore, their communication range and rate is limited, which a‚ects the quality of localization and, consequently, mapping. In addition, conducting real-time localization and mapping is not possible. As a result, Simultaneous Localization and Mapping (SLAM) techniques are not suitable and a new problem de€nition is needed. In this paper we introduce what we dub as the Centralized O„ine Localization And Mapping (COLAM) problem, highlighting its key elements, then we present a model to solve it. In this model evolutionary algorithms (EAs) are used to optimize agents’ resources o‚-line for an energy-ecient environment mapping. Furthermore, we illustrate a modi€ed version of Vietoris-Rips Complex we dub as Trajectory Incorporated Vietoris-Rips (TIVR) complex as a tool to conduct mapping. Finally, we project the proposed model on real experiments and present results.