On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities via miniaturized autonomous sensory agents

Miniaturized autonomous sensory agents (MASAs) can play a pivotal role in smart cities of tomorrow. From monitoring underground infrastructure such as pollution in water pipes, to exploration of natural resources such as oil and gas. These smart agents will not only detect anomalies, they are expected to provide sufficient data to facilitate mapping the detected anomalies, while—cleverly—adopting their behavior based on the changes presented in the environment. However, given these objectives and due to MASA’s miniaturization, conventional designing methods are not suitable to design MASA. On one hand, it is not possible to use the widely adopted simultaneous localization and mapping (SLAM) schemes, because of the hardware limitations on-board of MASA. Furthermore, the targeted environments for MASA in a smart city are typically GPS-denied, hardly accessible, and either completely or partially unknown. Furthermore, designing MASA’s hardware and their autonomous on-line behavior presents an additional challenge. In this chapter, we present a framework dubbed as evolutionary localization and mapping (EVOLAM), which uses multiobjective evolutionary algorithms (MOEAs) to tackle the design and algorithmic challenges in using MASAs in monitoring infrastructure. This framework facilitates offline localization and mapping, while adaptively tuning offline hardware constraints and online behavior. In addition, we present different types of MOEAs that can be used within the framework. Finally, we project EVOLAM on a case-study, thus highlighting MOEA effectiveness in solving different complex localization and mapping problems.