스마트 팩토리를 위한 제조공정 내에서 단조 부품의 이송자동화를 위한 로봇의 실시간 경로제어에 관한 연구

A Study on the Real-Tim Path Control of Robot for Transfer Automation of Forging Parts in Manufacturing Process for Smart Factory

Abstract

This paper proposed a new technology to control a path forging parts in limited narrow space of manufacturing process automation for smart factory. In the motion control, we adapted the obstacle avoidance technology based on ultrasonic sensors. The new motion control performance test for a mobile robot is experimented in narrow space environments. The travelling path control is performed by a fuzzy control logic. which plays a role for selecting an appropriate behavior in accordance with the situation in the vicinity of the mobile robot. Ultrasonic sensors installed at the front face of the mobile robot are used. In order to update the current position and heading angle of the mobile robot, a new approch is adapted. The reliability is illustrated by simulation and experiments.

Fig. 1 The coordinates system of robot (X-Y-Z axis)

Fig. 2 The exponential form of repulsive force potential

Fig. 3 Fuzzy logic system

Fig. 4 Schematic of robot controller

Fig. 5 The relation between of robot frame and wall

Fig. 6 Membership function for angle in path planning

Fig. 7 Membership function for distance in path planning

Fig. 8 Voice recognition scheme

Fig. 9 Control performance of the intelligent controller for the variation of the velocity trajectory with parameter uncertainties(8%)

Fig. 10 Control performance of the intelligent controller for variation of the velocity trajectory with parameter uncertainties (12%)

Fig. 11 Control performance of the intelligent controller for the variation of the azimuth trajectory with parameter uncertainties (8%)

Fig.12 Control performance of the intelligent controller for the variation of the azimuth trajectory with parameter uncertainties (12%)

Fig. 13 Voice recognition word registration program

Fig. 14 The scnen of experiment

Fig. 15 experiment result of transfer automation a forging parts

Table 1. Fuzzy rules 1 for path planning(Angle)

Table 2. Fuzzy rules 2 for path planning(Distance)

Table 3. The learning results of Ω_s for reference trajectory

Table 4. The 49 control rules for 7 labels