Navigation of humanoids by a hybridized regression-adaptive particle swarm optimization approach

In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids. The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment. The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments. The results obtained from both the environments are compared against each other, and a good agreement between them is observed. Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.