Earticle

초록

영어

The efficient deployment of robot-based manufacturing systems is frequently hindered by the substantial time required for programming collision-free robot paths during the commissioning process. This challenge involves intensive tasks such as teach-in, offline programming, and subsequent path optimization. To dramatically accelerate this critical stage, the industry needs an automatic and intelligent path planning system. This work introduces a novel system designed for the autonomous path planning of industrial robots. We conduct an explicit comparison between samplingbased methods such as probabilistic roadmaps (PRM) and rapidly exploring random Trees (RRT), and computational intelligence (CI) based methods, particularly genetic algorithms. Our findings demonstrate the potential for these advanced techniques to drastically reduce robot deployment time.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. AUTOMATIC ROBOT PATH PLANNING
A. Implementation
B. Path Planning
C. Sampling-based Planners
D. Evolutionary Algorithms
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
REFERENCES

키워드

저자

• Jonghwa Kim [ Department of Artificial Intelligence Cheju Halla University Jeju, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004