Quantum-Behaved Multi-Robot Plume Source Localization with Formation Maintenance and Obstacle Avoidance

Abstract

In combination with optimization theory and swarm evolution mechanisms, this paper addresses a plume source localization issue based on multiple mobile robots equipped with sensors. First of all, we offer the plume modeling process and analyze some internal characteristics about its source. Secondly, an adaptive weight is introduced into quantum particle swarm optimization (QPSO) algorithm, which makes quantum-behaved optimization (QBO) algorithm be proposed as a swarm search scheme for robots. Subsequently, based on the attractive/repulsive field theory, a collision avoidance strategy with formation maintenance and obstacle elusion is provided for robots on the move. Finally, we offer the analysis for the developed policy, such as the global convergence and computational complexity.