An optimal jerk-stiffness controller for gait pattern generation in rough terrain

In this paper, an optimal jerk stiffness controller is proposed to produce stable gait pattern generation for bipedal robots in rough terrain. The optimal jerk controller is different from the point-to-point and via-Point conventional approaches as trajectories are planned in the Cartesian space system whereas control laws are expressed in the joint space. Its major contribution resides in the generation of stable semi elliptic Cartesian trajectories during the swing phase that do combine benefits of trigonometric and polynomial functions. The stiffness controller is designed without gravity compensation and ensures for the robotic system elastic and stable contact forces with the ground during the impact and the double support phases. Not only, the control strategy proposed needs very few sensors to be implemented but also it ensures robustness to sensory noise and safety with rough terrain. Simulation performed on a 12 DOF bipedal robot shows the performances of the control laws combined to produce a 3D stable walking cycles without shaking in uneven terrain.