In the era of robotics, path planning and navigation of bipeds in multifaceted environments have always remained as one of the most promising areas of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both statistical technique and computational intelligence method for path planning of bipeds. The proposed path 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 bipeds in a cluttered environment. The proposed navigational controller is experimented for a bipedal robot in simulation and experimental environments. The outputs from various simulations and experimental results are compared and good agreement between navigational parameters like path length and path time is observed. Again a better efficacy has been observed by comparing the designed hybridization technique with an existing approach which validates the novel approach.