Sensor Filtering and State Estimation of a Fast Simulated Planar Bipedal Robot

The development of bipedal humanoid robots is a very prevalent area of research today. Legged robots have many advantages over wheeled robots on rough or uneven terrains. Due to the rapid growth in robotics, it is unavoidable that legged robots will be adapted for everyday household settings. However, the agile bipedal robots possesses many design and control challenges. Model based control of humanoid robots relies on the accuracy of the state estimation of the model’s constituents. The spring loaded inverted pendulum (SLIP) is frequently used as a fundamental model to analyze bipedal locomotion. In general, it consists of a stance phase and a flight phase, employing different strategies during these phases to control speed and orientation. Due to the underactuation and hybrid dynamics of bipedal robots during running, estimating the state of the robot’s appendages can be challenging. In this paper, various Kalman estimation techniques are combined with sensor data fusion to predict the spatial state of a fast simulated planar SLIP model.