An agent-based dynamic routing strategy for a generic automated material handling systems (AMHS) is developed. The strategy employs an agent-based paradigm by which the control points of a network of AMHS components are modelled as cooperating node agents. With the inherent features of route discovery, route selection and fault management of these node agents, their performance is investigated through simulation study. A generic AMHS network is modelled with a simulation tool that represents a highly flexible material handling system in a typical distribution centre where simulation studies are performed under normal and exception operating conditions. The performance of the proposed dynamic routing strategy is benchmarked with a number of static and dynamic routing strategies including the shortest distance, round robin, widest shortest route and shortest widest route strategies. The results of the simulation experiments are presented and their performance compared under a number of performance indices including the cycle time, utilization and ability to balance network loading.

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This paper presents a study on the calibration of accelerometer data in the gyroscope free inertial navigation system(GFINS) using fuzzy inference system(FIS). The conventional INS(inertial navigation system) which can measure yaw rate and linear velocity using inertial sensors as the gyroscope and accelerometer. However, the INS is difficult to design as small size and low power because it uses the gyroscope. To solve the problem, the GFINS which does not have the gyroscope have been studied actively. However, the GFINS has cumulative error problem still. Hence, this paper proposes Fuzzy-GFINS which can calibrate the data of an accelerometer using FIS consists of two inputs that are ratio between linear velocity of the autonomous ground vehicle(AGV) and the accelerometer and ratio between linear velocity of the encoders and the accelerometer. To evaluate the proposed Fuzzy-GFINS, we made the AGV with Mecanum wheels and applied the proposed Fuzzy-GFINS. In experimental result, we verified that the proposed method can calibrate effectively data of the accelerometer in the GFINS.

This paper presents to study the velocity control method of AGV for heavy material transport. Generally, in the industries, fork-type AGV using path tracking requires high stop-precision with performing operations for 20 hours. To obtain the high stop-precision of AGV for heavy material transport, AGV requires driving technic during low speed. Hence, we use encoder with keeping the speed of AGV and study the velocity control method to improve for the stop-precision of AGV. To experiment the proposed the velocity control method, we performed the experiments engaging the pallet located 4m in front of the AGV. In the experimental result, the maximum error of stop-precision was less than 18.64mm, and we verified that the proposed method is able to control stable.

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