Mobile Robot path planning using a teaching-learning-interactive learning-based optimization

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. © International Association of Engineers.