• Title/Summary/Keyword: Fork-type automatic guided vehicle

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Positioning and Driving Control of Fork-type Automatic Guided Vehicle With Laser Navigation

  • Kim, Jaeyong;Cho, Hyunhak;Kim, Sungshin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.4
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    • pp.307-314
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    • 2013
  • We designed and implemented a fork-type automatic guided vehicle (AGV) with a laser guidance system. Most previous AGVs have used two types of guidance systems: magnetgyro and wire guidance. However, these guidance systems have high costs, are difficult to maintain with changes in the operating environment, and can drive only a pre-determined path with installed sensors. A laser guidance system was developed for addressing these issues, but limitations including slow response time and low accuracy remain. We present a laser guidance system and control system for AGVs with laser navigation. For analyzing the performance of the proposed system, we designed and built a fork-type AGV, and performed repetitions of our experiments under the same working conditions. The results show an average positioning error of 51.76 mm between the simulated driving path and the driving path of the actual fork-type AGV. Consequently, we verified that the proposed method is effective and suitable for use in actual AGVs.

Path Tracking for AGV using Laser guidance system (레이저 유도 시스템을 이용한 AGV의 경로추적)

  • Park, Jung-Je;Kim, Jung-Min;Do, Joo-Cheol;Kim, Sung-Shin;Bae, Sun-Il
    • The Journal of Korea Robotics Society
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    • v.5 no.2
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    • pp.120-126
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    • 2010
  • This paper presents to study the path tracking method of AGV(autonomous guided vehicle) which has a laser guidance system. An existing automatic guided vehicles(AGVs) which were able to drive on wired line only had a automatic guidance system. However, the automatic guidance systems that those used had the high cost of installation and maintenance, and the difficulty of system change according to variation of working environment. To solve such problems, we make the laser guidance system which is consisted of a laser navigation and gyro, encoder. That is robust against noise, and flexible according to working environment through sensor fusion. The laser guidance system can do a perfect autonomous driving. However, the commercialization of perfect autonomous driving system is difficult, because the perfect autonomous driving system must recognize the whole environment of working space. Hence, this paper studied the path tracking of AGV using laser guidance system without wired line. The path tracking method is consisted of virtual path generation method and driving control method. To experiment, we use the fork-type AGV which is made by ourselves, and do a path tracking experiments repeatedly on same experimental environment. In result, we verified that proposed system is efficient and stable for actual fork-type AGV.

Improvement of Positioning Accuracy of Laser Navigation System using Particle Filter (파티클 필터를 이용한 레이저 내비게이션의 위치측정 성능 향상)

  • Cho, Hyun-Hak;Kim, Jung-Min;Do, Joo-Cheol;Kim, Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.6
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    • pp.755-760
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    • 2011
  • This paper presents a method for improving the positioning accuracy of the laser navigation. As a wireless navigation system, the laser navigation which is more flexible than a wired guidance system is used for the localization and control of an AGV(automatic guided vehicle). However, the laser navigation causes the large positioning error while the AGV turns or moves fast. To solve the problem, we propose the method for improving the positioning accuracy of the laser navigation using particle filter which has robust and reliable performance in non-linear/non-gaussian systems. For the experiment, we use the actual fork-type AGV. The AGV has a gyro, two encoders and a laser navigation. To verify the performance, the proposed method is compared with the laser navigation which is a product. In the experimental result, we verified that the proposed method could improve the positioning accuracy by approximately 66.5%.