Abstract
An intelligent material-handling system is an important part of an autonomous decentralized manufacturing system(ADMS). Especially, an automatically guided vehicle(AGV)is the core of the material-handling system. This paper reports on a method to drive the AGV autonomously in the ADMS. The new method proposed here combines the sparse distributed memory neural network(SDM)with Q-learnig(Q-L). The SDM explores and acquires feature scenes required for AGV driving. The Q-L finds a direction to take at a particular scene acquired by the SDM. Numerical simulation verifies that the SDM can extract the feature scenes necessary for driving the AGV, and that the Q-L can instruct the right direction to the AGV at each extracted scene so that the vehicle may move towards the target location.
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