Navigating a robotic swarm in an uncharted 2D landscape

Navigation is a major issue in robotics due to the necessity for the robots' course of movement. Navigation consists of two essential components known as localization and planning. Localization in robotics refers to one's location with reference to a well known position inside the map. Planning is considered as the computation of a path through a map which represents the environment. This given path would be chosen based on the potential of the problem so that the expected destination would be achieved. As such, a reliable map is essential for navigation without which robots would not be able to accomplish the goals. In navigational approaches, reliability of the map would be challenged due to the dynamic and unpredictable nature of real-world applications. It is, consequently, crucial to implement solutions for searching such environments-those affected by dynamic and noisy constraints. In the present study, two enhanced versions of particle swarm optimization (PSO) called area extension PSO (AEPSO) and cooperative AEPSO (CAEPSO) are employed. During the study, AEPSO and CAEPSO are employed as decision-makers and movement controllers of simulated robots (hereafter referred to as agents). The agents' task is to seek for survivors in realistic simulations based on real-world hostile situations. This study examines the feasibility of AEPSO and CAEPSO on uncertain and time-dependent simulated environments. The simulations follow two phases of training and testing model. Agents use past knowledge gathered during the training phase in their testing phase. The study addresses the impacts of past knowledge, homogeneity and heterogeneity in robotic swarm search. The results demonstrate the feasibility of CAEPSO as robot controller and decision-maker.