AGV autonomous driving based on scene recognition acquired by simplified SDM

An intelligent material handling system plays a great role in an autonomous decentralized manufacturing system (ADMS). An automatically guided vehicle (AGV) is at the center of the intelligent material handling system. This paper reports on a method for autonomously driving the AGV in the ADMS. A new method is proposed that combines the sparse distributed memory neural network (SDM) with Q-learning (Q-L). The SDM is adopted to explore and acquire scenes required for AGV driving. Q-L is employed to find a direction at the scene acquired by SDM. Numerical simulations verify that the SDM can extract the feature scenes necessary to drive the AGV and that Q-L instructs the suitable direction to the AGV at the extracted scenes towards the target location through its driving experiences