面向四足机器人团队的安全关键型分布式非线性预测控制器
Safety-Critical and Distributed Nonlinear Predictive Controllers for Teams of Quadrupedal Robots
AI总结：
针对多四足机器人协同运动的安全保障难题，提出融合CBF与一致性协议的分布式NMPC分层控制框架，经仿真与硬件实验验证，其成功率比传统无CBF约束的NMPC高27.89%。
摘要：
本文提出了一种新颖的分层安全关键控制框架，该框架将分布式非线性模型预测控制器（DNMPC）与控制屏障函数（CBF）相集成，以实现多智能体四足机器人在复杂环境中的协同运动。尽管基于NMPC的方法已被广泛用于施加安全约束并引导多机器人系统（MRS）穿越复杂环境，但要保障MRS的安全性，需要基于不变集概念给出形式化定义。通常在规划层通过二次规划（QP）实现的CBF可提供形式化安全保证，但其零控制时域限制了其在固有不稳定、欠驱动的非线性腿式机器人模型上进行长时域轨迹规划的有效性。此外，将CBF集成到四足机器人团队这类复杂MRS的实时NMPC中的研究仍不充分。本文开发了计算高效的分布式NMPC算法，该算法在一致性协议内融入了基于CBF的碰撞安全保证，使得机器人能够在存在扰动和崎岖地形的条件下，以更长的规划时域实现安全协同运动。DNMPC生成的最优轨迹由底层的全阶非线性全身控制器进行跟踪。本文通过多达4台Unitree A1机器人的大量数值仿真，以及2台A1机器人在受外部推力、崎岖地形和障碍物信息不确定条件下的硬件实验，验证了所提方法的有效性。对比分析表明，所提出的基于CBF的DNMPC比无CBF约束的传统NMPC成功率高出27.89%。
英文摘要：
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.
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