Safety-Critical and Distributed Nonlinear Predictive Controllers for Teams of Quadrupedal Robots

2025-03-18

AI Summary

Ensuring safe, coordinated locomotion for legged robot swarms operating under disturbances, uneven terrain, and uncertain obstacle information remains challenging. Method: This paper proposes a hierarchical safety control framework that integrates Control Barrier Functions (CBFs) into real-time distributed nonlinear Model Predictive Control (DNMPC) for the first time—overcoming the conventional zero-horizon limitation of CBFs and enabling long-horizon safety planning for underactuated legged robots. The approach extends the prediction horizon via multi-agent consensus protocols and ensures closed-loop robustness through nonlinear full-body tracking and quadratic programming (QP)-based optimization. Results: Hardware experiments with two A1 quadrupeds demonstrate disturbance rejection, obstacle traversal, and terrain adaptation. In four-robot simulations, task success rate improves by 27.89% over an unconstrained NMPC baseline, validating significant performance gains from CBF-enforced safety constraints.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a novel hierarchical, safety-critical control framework that integrates distributed nonlinear model predictive controllers (DNMPCs) with control barrier functions (CBFs) to enable cooperative locomotion of multi-agent quadrupedal robots in complex environments. While NMPC-based methods are widely adopted for enforcing safety constraints and navigating multi-robot systems (MRSs) through intricate environments, ensuring the safety of MRSs requires a formal definition grounded in the concept of invariant sets. CBFs, typically implemented via quadratic programs (QPs) at the planning layer, provide formal safety guarantees. However, their zero-control horizon limits their effectiveness for extended trajectory planning in inherently unstable, underactuated, and nonlinear legged robot models. Furthermore, the integration of CBFs into real-time NMPC for sophisticated MRSs, such as quadrupedal robot teams, remains underexplored. This paper develops computationally efficient, distributed NMPC algorithms that incorporate CBF-based collision safety guarantees within a consensus protocol, enabling longer planning horizons for safe cooperative locomotion under disturbances and rough terrain conditions. The optimal trajectories generated by the DNMPCs are tracked using full-order, nonlinear whole-body controllers at the low level. The proposed approach is validated through extensive numerical simulations with up to four Unitree A1 robots and hardware experiments involving two A1 robots subjected to external pushes, rough terrain, and uncertain obstacle information. Comparative analysis demonstrates that the proposed CBF-based DNMPCs achieve a 27.89% higher success rate than conventional NMPCs without CBF constraints.

Problem

• Enables safe cooperative locomotion for quadrupedal robot teams.
• Integrates CBFs with DNMPCs for collision safety guarantees.
• Improves success rate in complex environments with disturbances.

Innovation

• Hierarchical control integrates DNMPCs with CBFs.
• CBF-based safety guarantees enhance collision avoidance.
• Distributed NMPC enables safe multi-robot locomotion.