Novel Gmapping Using Quantum-Behaved Particle Swarm Optimization *

This paper proposed quantum-behaved particle swarm optimization (QPSO) combined with Gmapping simultaneous localization and mapping (SLAM). In quantum mechanics, the positions and velocities of particles cannot be determined simultaneously due to the uncertainty principle. Therefore, in the QPSO algorithm, the positions of particles must be randomly initialized. Every particle is described by a wave function's probability density function, which helps particles find optimal solutions. Based on the sampling method of QPSO combined with Gmapping., it can help particles find the larger solution space. The scan matching algorithm must be used as the fitness function., where the Gmapping focuses on the data of particles with high fitness values. Then, the error of building a digital map can be reduced, which can decrease the risk of crashing obstacles.