Optimization of stability of humanoid robot NAO using ant colony optimization tuned MPC controller for uneven path

The primary conventional method for simplifying legged robots’ complex walking dynamics involves using low-dimensional models such as the linear inverted pendulum model (LIPM). This paper emphasizes utilizing the LIPM plus flywheel model (LIPPFM) for analysis of the complete dynamic motion of the humanoid robot. Inclining toward a more realistic case, the model is improvised to remove the COM’s height constraint (center of mass) and consider the effect of the upper body part using the mass of the pendulum. Furthermore, the double support phase is being discussed in the locomotion phase of the humanoid robot. MPC (model predictive control) approach has been used in this paper, which is tuned with the ACO (ant colony optimization) technique. The desired trajectory, joint angles, has been imparted to the MPC, which provides the robot’s joint motion. This joint motion has been further transferred to ACO, optimizing the step adjustment and providing an expected trajectory to walk over an uneven surface. The simulation has been carried out in an uneven environment based on ACO tuned MPC controller, and further, it has been validated using real-time experiments on humanoid robot NAO. The controller shows a reasonable degree of efficiency in both the real NAO and simulated NAO with a deviation under 5%. The comparative study among various controller shows that the proposed controller lowers the peak overshoot and the settling time. In comparison with the previously developed controller, the deviation in roll angle and pitch angle justifies the selection of the proposed controller.