A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller

Abstract

Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.