In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.