Dual-MPC Footstep Planning for Robust Quadruped Locomotion

2025-11-11

AI Summary

Unintended body rotation and oscillation in quadrupedal locomotion arise from inadequate modeling of angular velocity dynamics. Method: This paper proposes a footstep–force co-planning framework based on model predictive control (MPC). It innovatively reformulates angular momentum regulation as a joint optimization problem over foot placement and ground reaction forces, embedding a two-layer feedback architecture that integrates an extended linear inverted pendulum model with angular velocity coupling constraints, and introduces an iterative mutual feedback mechanism between gait planning and force control. Contribution/Results: Experiments demonstrate that the method significantly suppresses undesired body rotation and oscillation across diverse complex terrains, extends stance and swing phase durations, and enhances posture stability and locomotion robustness—establishing a novel paradigm for dynamic angular momentum regulation in quadrupedal robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we propose a footstep planning strategy based on model predictive control (MPC) that enables robust regulation of body orientation against undesired body rotations by optimizing footstep placement. Model-based locomotion approaches typically adopt heuristic methods or planning based on the linear inverted pendulum model. These methods account for linear velocity in footstep planning, while excluding angular velocity, which leads to angular momentum being handled exclusively via ground reaction force (GRF). Footstep planning based on MPC that takes angular velocity into account recasts the angular momentum control problem as a dual-input approach that coordinates GRFs and footstep placement, instead of optimizing GRFs alone, thereby improving tracking performance. A mutual-feedback loop couples the footstep planner and the GRF MPC, with each using the other's solution to iteratively update footsteps and GRFs. The use of optimal solutions reduces body oscillation and enables extended stance and swing phases. The method is validated on a quadruped robot, demonstrating robust locomotion with reduced oscillations, longer stance and swing phases across various terrains.

Problem

• Optimizing footstep placement to regulate body orientation against rotations
• Addressing angular momentum control through dual-input coordination of forces and footsteps
• Reducing body oscillations and extending stance phases for robust quadruped locomotion

Innovation

• MPC footstep planning optimizes body orientation control
• Dual-input approach coordinates footstep placement and ground reaction forces
• Mutual-feedback loop iteratively updates footsteps and forces