Terrain-Aware Hierarchical Control Framework for Dynamic Locomotion of Humanoid Robots

Dynamic locomotion capabilities on complex terrains constitute a critical requirement for humanoid robots in industrial manufacturing and emergency response applications. To address the fundamental challenges of terrain perception, underactuated dynamics planning, and foothold control in unstructured environments, this paper proposes a hierarchical planning and control framework that integrates terrain perception. The framework first segments the terrain to generate convex polygon constraints that characterize the terrain features. Subsequently, an optimization model is constructed based on nonlinear model predictive control, integrating underactuated dynamics and terrain constraints into a multi-objective optimal control problem. This problem is solved in real time using sequential quadratic programming. Furthermore, a hierarchical whole-body control approach is employed, which achieves coordinated whole-body control under multiple tasks and constraints through priority task allocation and quadratic programming. We validate the proposed methods through simulated experiments on forward walking, external force disturbance, and complex terrain walking conducted on the MuJoCo simulation platform. The simulation demonstrates that the robot can achieve a stable walking speed of 1 m/s and possess forward and lateral anti-disturbance capabilities of 60 Ns and 30 Ns, respectively. The robot can also stably traverse stairs with a height difference of 0.16 m and random terrains. These results validate the advantages of the proposed method in terms of dynamic performance, robustness, and terrain adaptability.