Evaluation of metaheuristic optimization algorithms applied to path planning

Abstract

The development of autonomous ground vehicles is a topic widely discussed by the international scientific community. Path planning for these robots is an essential element to achieving a satisfactory fulfillment of missions without human intervention. This work adopts and improves the metaheuristic optimization methods harmony search and ant colony optimization, developing strategies based on the enhanced harmony search and improved ant colony optimization to achieve short, collision-free paths for robot movement. A description of the original methods and improvement procedures is presented, and their performance in path planning is compared through simulation. Theoretical results are supported by simulated experiments, which highlight the situations in which each metaheuristic algorithm is preferable and the benefits they bring to the path planning strategy.