- 권한신청
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS
An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments
An Optimized Random Tree and Particle Swarm Algorithm For Distribution Environments
Lee, Un-Kon (School of Economic and Business Administration, The University of Suwon)
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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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