Safety-Critical and Distributed Nonlinear Predictive Controllers for Teams of Quadrupedal Robots
Basit Muhammad Imran, Jeeseop Kim, TAIZOON ALIASGAR CHUNAWALA, Alexander Leonessa, Kaveh Akbari Hamed
AI summary
Problem
Existing control frameworks struggle to provide formal safety guarantees and computational efficiency for multi-agent quadrupedal robots, as separate CBF and NMPC layers limit planning horizons and real-time performance.
Approach
The authors develop a unified hierarchical framework that embeds discrete-time higher-order Control Barrier Functions directly into a real-time distributed nonlinear MPC, using a consensus potential function to coordinate robot teams while tracking trajectories with a low-level whole-body controller.
Key results
- Unified CBF-DNMPC framework operates at 100 Hz for real-time planning
- Collision avoidance success rate increases from 66% to 94% over baselines
- Validated on up to four simulated and two physical Unitree A1 robots
- Outperforms separate high/low-level CBF-QP and linear inverted pendulum approaches
Why it matters
Provides a scalable, safety-guaranteed control architecture for cooperative legged robot teams, critical for deployment in unstructured disaster response and exploration missions.
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 used to enforce safety constraints and navigate multi-robot systems (MRSs) through complex environ- ments, trajectory optimization frameworks based on invariant sets offer formal safety guarantees for MRSs. 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, en- abling 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 results demonstrate that the proposed CBF-integrated DNMPC achieves a higher success rate than baseline NMPCs employing CBFs at the high or low-level layers.