BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
This work addresses motion planning for legged robots in dynamic environments, representing an incremental improvement by applying a nonlinear MPC framework to enhance online performance and adaptability.
The authors tackled the challenge of online whole-body motion planning for legged robots by proposing BiConMP, a nonlinear MPC framework that efficiently exploits robot dynamics structure, enabling the generation of cyclic gaits, handling pushes, and transitioning between gaits on real quadruped robots, with extensions to humanoid and other quadruped robots in simulation.
Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear MPC framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes and transitioning online between different gaits. Further, the ability of BiConMP to generate non-trivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.