Nonlinear model predictive control for rough-terrain robot hopping

This paper examines and quantifies the theoretical efficacy of a limited look-ahead strategy for hopping robots on rough terrain. Here, a classic spring-loaded inverted pendulum (SLIP) hopper and an actuated, lossy SLIP (ALSLIP) hopper with a more realistic dynamic model that includes an unsprung mass and a series-elastic actuator are each analyzed under conditions where the desired footholds are predetermined according to a stochastic process. We examine the effect of the length of the horizon on the accuracy of foot placement, and we test the robustness of the approach to model uncertainties. Our simulation results show that a model predictive control (MPC) approach is an effective technique for foothold selection, and that a two-step planning horizon for upcoming terrain is theoretically adequate for practical footstep planning in realistically noisy rough terrain running conditions.