Walking Pattern Trajectory Planning and Control Based on Energy Function and Inverse Pendulum Model for Biped Robot

In many research of biped robotics, for controlling the walking stability of the robot, the hip trajectory is planned at the same height, so the knees of the robot would bend while walking. However, this does not make sense when humans walk.
To deal with the problem, in this thesis, we develop a walking pattern generating algorithm, which would enable the biped robot walking like human (stretch knee walking). First of all, we build a 3D biped robot model by using SOLIDWORKS, and then convert it into gravitational simulation of Simmechanics of Malab software. We generate its walking pattern by the trajectory generator based on the algorithm of this research, and tune its two parameters of stability until the robot in simulation can walk. Finally, we apply the two parameters to the trajectory generator of the actual biped robot to generate the stable pattern. However, there exist modeling errors between the actual biped robot and the model in simulation. Therefore, although we have refined the two parameters in simulation, they still have to be adjusted slightly according to actual walking postures (fall down forward or backward). For this reason, we can use high speed camera to get the actual walking information, and tune the parameters slightly according to the methods in the thesis. Finally, we would enable the robot to walk.
From our simulation and experiment, the trajectory generator based on our algorithm indeed can generate several stable patterns with different step length and step time, and these knee-stretching patterns are different from the most biped robots in the world (such as ASIMO and HRP series), which walk with knee-bending patterns.
Furthermore, from the snapshots and their illustrations, we can understand the process and meaning in each snapshot. Each snapshot represents a moment of the time axis in our algorithm, and we can see there are many similar points with the human walking behavior from the corresponding separated behavior in each snapshot.