An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments

Feng, Zhou (College of Electronic Information, Shandong Institute of Commerce and Technology, Economic and Business Administration, The University of Suwon)
Lee, Un-Kon (School of Economic and Business Administration, The University of Suwon)

Feng, Zhou, & Lee, Un-Kon. (2015). An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments. The Journal of Distribution Science(JDS), 13(6), 11-15, https://doi.org/https://doi.org/10.15722/jds.13.6.201506.11

복사

Abstract

Purpose - Robot path planning, a constrained optimization problem, has been an active research area with many methods developed to tackle it. This study proposes the use of a Rapidly-exploring Random Tree and Particle Swarm Optimizer algorithm for path planning. Research design, data, and methodology - The grid method is built to describe the working space of the mobile robot, then the Rapidly-exploring Random Tree algorithm is applied to obtain the global navigation path and the Particle Swarm Optimizer algorithm is adopted to obtain the best path. Results - Computer experiment results demonstrate that this novel algorithm can rapidly plan an optimal path in a cluttered environment. Successful obstacle avoidance is achieved, the model is robust, and performs reliably. The effectiveness and efficiency of the proposed algorithm is demonstrated through simulation studies. Conclusions - The findings could provide insights to the validity and practicability of the method. This method makes it is easy to build a model and meet real-time demand for mobile robot navigation with a simple algorithm, which results in a certain practical value for distribution environments.

keywords: Robot, Path Planning, Rapidly-exploring, Random Tree Particle, Swarm Optimizer

참고문헌

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논문 상세

Vol.13 No.6

An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments

An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments

Abstract

참고문헌

The Journal of Distribution Science(JDS)