Abstract
AbstractAutonomous robots are employed in manufacturing units and industries as they can perform complex tasks in hazardous and harmful environments such as space explorations, submarines, and mining work. When a robot can traverse from start to goal location while evading obstacles, it is called an autonomous robot for which path planning is essential. All evolutionary and swarm intelligence approaches are probabilistic-based and entail few to many control parameters based on problem and application area. Shuffled frog leaping algorithm (SFLA) is one such example that requires six problem-specific parameters, while teaching learning-based optimization (TLBO) is a specific parameter-less algorithm. Thus, there is no burden of tuning control parameters in TLBO. In this work, comparison of both these algorithms is done on MATLAB 2018a to demonstrate the results that despite the usage of no specific parameters, TLBO outperforms SFLA algorithm for path planning and obstacle avoidance problem (PPOA).KeywordsPath planningObstacle avoidanceShuffled frog leaping algorithmTeaching learning based optimization
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