Impact Force Location and Intensity Identification Using Joint-Position Sensors in Humanoids

Abstract

Humanoid robots are capable of imitating humans, adapting to changes in an environment, making decisions, and performing tasks. But they are also at a high risk of being hit by an external impact force or disturbance. However, unlike humans, robots do not have sensory neurons to be able to sense the location at which they were hit as well as the intensity of the impact force. In this paper, humanoids are tested for the ability to perceive their surrounding better by recognizing the impact force location and intensity after monitoring the response of the encoders at the different joints due to an external disturbance where additional sensors to the robot are not needed. Using machine learning, several models are trained and then tested to ameliorate and increase the robustness of the stability control algorithm as it can help the robot regain stability in a more educated system. Giving the humanoid the ability to perceive its surroundings better also gives it the ability to react and control its movement and posture with a better and faster response despite the increasing number of degrees of freedom which makes controlling the robot more difficult.