TY  - JOUR
AB  - Purpose This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment.Design/methodology/approach The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method.Findings Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method.Originality/value Therefore, this can provide a new method for the area of path planning of intelligent robot.
VL  - 36
IS  - 2
SN  - 0144-5154
DO  - 10.1108/AA-10-2015-079
UR  - https://doi.org/10.1108/AA-10-2015-079
AU  - Zeng Nianyin
AU  - Zhang Hong
AU  - Chen Yanping
AU  - Chen Binqiang
AU  - Liu Yurong
PY  - 2016
Y1  - 2016/01/01
TI  - Path planning for intelligent robot based on switching local evolutionary PSO algorithm
T2  - Assembly Automation
PB  - Emerald Group Publishing Limited
SP  - 120
EP  - 126
Y2  - 2024/04/24
ER  -