Int. J. of Wireless and Mobile Computing   »   2015 Vol.9, No.4

 

 

Title: A humanoid robot path planning method based on virtual force-directed particle swarm optimisation

 

Authors: Xiandong Xu; Yi Guan; Bingrong Hong; Wende Ke; Songhao Piao

 

Addresses:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

 

Abstract: In this paper, a path planning method for a humanoid robot based on virtual force-directed Particle Swarm Optimisation (PSO) is presented. We take each obstacle as a source of repulsive force affecting the humanoid robot, and the target as a source of attractive force, combining PSO with footstep planning method to find a path for the robot. We aim at avoiding the slow convergence of PSO path planning and affect the speed and the process of updating particles of PSO. A method that can guide the particle evolution and accelerate the convergence of the algorithm by the virtual force between the mobile humanoid robot and obstacles or destination is presented. Experiment shows that this method can effectively realise humanoid robot path planning. Compared with the basic particle swarm path planning algorithm, the convergence speed of the virtual force-directed particle swarm path planning method is faster and less time-consuming.

 

Keywords: particle swarm optimisation; PSO; virtual force; humanoid robots; robot path planning; mobile robots; convergence speed.

 

DOI: 10.1504/IJWMC.2015.074035

 

Int. J. of Wireless and Mobile Computing, 2015 Vol.9, No.4, pp.325 - 331

 

Submission date: 09 Jun 2015
Date of acceptance: 07 Jul 2015
Available online: 02 Jan 2016

 

 

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