Biologically-Inspired Neural Network for Walking Stabilization of Humanoid Robots

In order to accomplish desired tasks, humanoid robots may have to deal with unpredicted disturbances, generated by objects, people and even ground imperfections. In some of these cases, foot placement is critical and cannot be changed. Furthermore, the robot has to conduct the actions planned meanwhile stabilizing its walking motion. Therefore, we propose a Biologically-inspired Neural Network (BiNN) to stabilize the walking motion of humanoid robots by ankle joint control, which minimally affects the current movements of the robot. In contrast to other neural networks, which only generate walking patterns, the BiNN is adaptive, as it compensates disturbances during the robot motion. Moreover, the BiNN has a low computational time and can be used as a module of other control methods. This approach was evaluated with Webots simulator, presenting improvements in the compensation of an external force in regard to its magnitude and duration.