Centroidal Trajectory Generation and Stabilization Based on Preview Control for Humanoid Multi-Contact Motion

2022-07-01

IEEE Robotics and Automation Letters

AI Summary

This work addresses the challenge of online centroidal momentum (CoM) trajectory generation and stability control for humanoid robots executing multi-contact dynamic motions in complex environments. We propose a lightweight hierarchical control framework based on preview control, replacing computationally intensive full-horizon constrained model predictive control with a low-complexity preview controller. The method integrates CoM dynamics modeling, state-feedback correction, and optimized contact wrench distribution to explicitly satisfy multi-contact constraints and stability requirements—while ensuring real-time performance (millisecond-level computation). Simulation results demonstrate significant improvements in robustness and trajectory tracking accuracy during representative dynamic tasks, including stepping and support-phase transitions. The approach establishes an efficient and practical paradigm for high-dynamic, multi-contact motion control of humanoid robots.

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Multi-contact motion is important for humanoid robots to work in various environments. We propose a centroidal online trajectory generation and stabilization control for humanoid dynamic multi-contact motion. The proposed method features the drastic reduction of the computational cost by using preview control instead of the conventional model predictive control that considers the constraints of all sample times. By combining preview control with centroidal state feedback for robustness to disturbances and wrench distribution for satisfying contact constraints, we show that the robot can stably perform a variety of multi-contact motions through simulation experiments.

Problem

• Generates centroidal trajectories for humanoid multi-contact motion
• Reduces computational cost using preview control
• Ensures stability with disturbance-robust feedback

Innovation

• Centroidal online trajectory generation and stabilization
• Preview control reduces computational cost
• Combines centroidal feedback and wrench distribution