Joint friction estimation and slip prediction of biped walking robots

Friction is a nonlinear and complex phenomenon. It is unwanted at the biped joints since it deteriorates the robot’s walking performance in terms of speed and dynamic behavior. On the other hand, it is desired and required between the biped feet and the walking surface to facilitate locomotion. Further, friction forces between the feet and the ground determine the maximum acceleration and deceleration that the robot can afford without foot slip. Although several friction models are developed, there is no exact model that represents the friction behavior. This is why online friction estimation and compensation enter the picture. However, when online model-free estimation is difficult, a model-based method of online identification can prove useful. This thesis proposes a new approach for the joint friction estimation and slip prediction of walking biped robots. The joint friction estimation approach is based on the combination of a measurementbased strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, it utilizes the force and acceleration measurements in a reduced dynamical model of the biped. The latter adopts a friction model to represent the joint friction when the leg is swinging. The model parameters are identified adaptively using the estimated online friction whenever the foot is in contact. Then the estimated joint friction contributes to joint torque control signals to improve the control performance. The slip prediction is a model-free friction-behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed estimation approaches are validated by experiments on SURALP (Sabanci University Robotics Research Laboratory Platform) and simulations on its model. The results demonstrate the effectiveness of these methods