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- Title
Mobile Robot Path Planning using a Teaching-Learning-Interactive Learning-Based Optimization.
- Authors
Yu-Huei Cheng; Pei-Ju Chao; Che-Nan Kuo
- Abstract
In many automated industrial environments, mobile robots have been widely used for performing exclusive tasks. Collision-free path planning is one of the most basic requirements for the application of mobile robots. In order to find a collision-free path in a known static environment for a mobile robot, a Teaching-Learning-Interactive Learning-Based Optimization (TLILBO) is proposed. The proposed method is a novel stochastic search algorithm modelled based on the process of natural selection. The proposed method is designed based on the three concepts of "teaching", "learning", and "interactive learning" to effectively search for a feasible and collision-free path. Two obstacle environmental maps retrieved from the literature were verified in this study. Simulation results showed that the proposed method was effective for path planning.
- Subjects
MOBILE robots; ROBOTIC path planning; INTERACTIVE learning; ENVIRONMENTAL mapping; NATURAL selection; SEARCH algorithms; MOBILE apps
- Publication
IAENG International Journal of Computer Science, 2019, Vol 46, Issue 2, p199
- ISSN
1819-656X
- Publication type
Article