Cooperative Area Extension of PSO - Transfer Learning vs. Uncertainty in a Simulated Swarm Robotics

Abstract

Navigation in dynamic and uncertain environments in the absence of reliable environment map is challenging. In this study, we investigate the effectiveness of two variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in noisy environments in which the noise does not represent random noise originated from a single type of source but the combination of noises originating from different sources located in nearby or faraway positions. Knowledge Transfer and Transfer Learning that represent the use of the expertise and knowledge gained from previous experiments can improve the robots decision making and reduce the number of wrong decisions in such uncertain environments. This study investigates the impact of transfer learning on robots’ search in such hostile environment. The results highlight the feasibility of CAEPSO to be used as the movement controller and decision maker of a swarm of robots in the simulated uncertain environment when gained expertise from past trainings is transferred to the robots in the testing phase.