Sensor Filtering And Position Tracking Of A Simulated Planar Bipedal Robot

The development of a humanoid robot is a very prevalent area of research today. Legged robots have many advantages over wheeled robots on rough or uneven terrains, and are more suitable for an everyday household setting, however they possess many design and control challenges. 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, estimating the state of the robot and its appendages can be challenging. In this paper, various Kalman estimation techniques are used to predict the state of a simulated planar SLIP model. The state estimations utilize only simulated sensor data, as if the simulated model was a physical one.