Research on PAGV path planning based on artificial immune ant colony fusion algorithm

Abstract

With the rapid development of modern science and technology, more and more high-tech has appeared in front of the common people, in particular, the recent rise of artificial intelligent robots and other supernova scientific developments. In order to move the robot according to the direction of the system design, it is necessary to plan the robot’s walking path to a certain extent; this requires the use of specific algorithms to achieve. This paper was based on the application of path planning at the present stage, the artificial immune and ant colony fusion algorithm were used to plan and analyze the AGV path, the ant colony algorithm based on the artificial immune algorithm to enhance the recognition function of the immune system and the ant searching for food secretion pheromone as the optimal path finding method was the basic algorithm developed in this paper, the path planning of AGV was analyzed and studied.

Keywords

Immune algorithm path planning ant colony algorithm

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References

J.Z.

Huang and

Y.W.

Cen , A path-planning algorithm for AGV based on the combination between ant colony algorithm and immune regulation, Advanced Materials Research422(16) (2017), 3–9.

Li ,

Dong ,

Li , et al., Study on robot path collision avoidance planning based on the improved ant colony algorithm.international conference on intelligent human-machine systems and cybernetics, IEEE, (2016).

Shen , Implementation of the AVG path planning based on the ant colony algorithms, Information Technology (2011).

Yue ,

Shen and

Zhu , Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics, International Journal of Computer Science & Information Tedinolo (2014).

Y.B.

Hou ,

Y.Q.

Yuan and

B.P.

Li , Path optimization for automatic guided vehicle based on fusion algorithm of particle swarm and ant colony, Applied Mechanics & Materials182-183(2015), 1452–1457.

Z.W.

Sun ,

X.K.

Liu and

X.D.

Wu , et al., Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft, Guangxue Jingmi Gongcheng/optics & Precision Engineering21(12) (2016), 3308–3316.

Liu .

Xiao and

X.Z.

Sha , The research on path planning of wall climbing robot based on ant colony algorithm and minimum gravity consumption algorithm, Applied Mechanics & Materials44-47(6) (2017), 414–424.

Wen ,

Zhang and

Liu , Path planning for laser navigation wheeled robots based on improved ant colony algorithm, Machinery & Electronics34(5) 2016), 73–76.

Zhu ,

Dong ,

Yan , et al., Research for the path planning of the agricultural robot based on the improved ant colony algorithm, Journal of Agricultural Mechanization Research (9) (2016), 48–52.

10.

Saidi-Mehrabad ,

Dehnavi-Arani ,

Evazabadian , et al., An ant colony algorithm (ACA) for solving the new integrated model of job shop scheduling and conllict-free routing of AGVs, Computers & Industrial Engineering86(C) (2014), 2–13.