A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment

Humanoids are preferred over their wheeled counter parts because of their ability to replace human efforts. Navigation and path planning of humanoids is very much important and challenging area of investigation for robotic researchers to enable the humanoids for accomplishing tedious and repetitive tasks. In this paper, a novel hybridization scheme is attempted for the path planning and navigation of humanoids in a cluttered environment. Here, hybridization has been attempted on NAO humanoid robots using regression technique and adaptive ant colony optimization. In the hybridization scheme, the navigational parameters of the humanoids are fed to the regression controller initially in terms of obstacle distances, and the interim output from the regression controller is again fed to the adaptive ant colony optimization controller to obtain the final output. By using V-REP software, navigation simulations are performed, and the simulation results are also tested against real experimental set-up developed under laboratory conditions. The simulation and experimental results reveal that the humanoids are successful in avoiding the obstacles and reach their destinations safely with path optimization. The results obtained from both the environments are compared against each other and are in good agreement with minimal percentage of errors. The navigational controller is tested for both single as well as multiple humanoids, and it works well for both the cases. Finally, the proposed controller is validated against other navigational approaches, and a significant enhancement of results is obtained.