A hybridized RA-APSO approach for humanoid navigation

With an increasing demand in the field of industrial automation, robotics research has occupied a significant attention of people dealing with automation technology. In this paper, a hybridization technique is proposed combining regression analysis with adaptive particle swarm optimization for navigation of humanoids. In context of humanoid navigation, sensory information regarding obstacle distances are fed as input parameters to a basic regression controller and the output of the regression controller is again fed as input to the adaptive particle swarm optimization controller to obtain the final output. The final output of the hybridized controller acts as the controlling factor for humanoid navigation in a complex environment. The logic of the proposed hybridized controller is tested in both simulated and experimental environments and the results obtained from both the environments are compared against each other with a good agreement between them. Finally, the proposed controller is also tested against other existing navigational techniques to validate the efficiency.