多四足机器人的安全分布式学习增强预测控制
Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots
AI总结：
本文提出一种分布式模型预测控制框架，结合控制李雅普诺夫函数、控制屏障函数、SAPIE编码、低延迟通信协议和死锁消解机制，实现多四足机器人稳定编队与无碰撞导航，并通过仿真和XG机器人实验验证其有效性。
摘要：
四足机器人在非结构化环境中表现出卓越的适应性，使其非常适合实际应用中的编队控制。然而，由于动态障碍物、通信约束以及腿足运动的复杂性，在保持稳定编队的同时确保无碰撞导航面临重大挑战。本文提出了一种用于多四足机器人编队控制的分布式模型预测控制框架，集成控制李雅普诺夫函数以确保编队稳定性，并采用控制屏障函数实现分散式安全约束。为应对团队结构动态变化的挑战，我们引入了尺度自适应置换不变编码（Scale-Adaptive Permutation-Invariant Encoding，SAPIE），该编码能够在保持置换不变性的同时，对邻近机器人进行鲁棒的特征编码。此外，我们开发了一种基于数据分发服务（Data Distribution Service）的低延迟通信协议和一种事件触发的死锁消解机制，以增强实时协调并防止在受限空间中的运动停滞。我们的框架通过NVIDIA Omniverse Isaac Sim中的高保真仿真以及使用我们自研四足机器人系统XG进行的实际实验得到了验证。结果表明，该框架实现了稳定的编队控制、实时可行性和有效的避障，验证了其大规模部署的潜力。
英文摘要：
Quadrupedal robots exhibit remarkable adaptability in unstructured environments, making them well-suited for formation control in real-world applications. However, keeping stable formations while ensuring collision-free navigation presents significant challenges due to dynamic obstacles, communication constraints, and the complexity of legged locomotion. This paper proposes a distributed model predictive control framework for multi-quadruped formation control, integrating Control Lyapunov Functions to ensure formation stability and Control Barrier Functions for decentralized safety enforcement. To address the challenge of dynamically changing team structures, we introduce Scale-Adaptive Permutation-Invariant Encoding (SAPIE), which enables robust feature encoding of neighboring robots while preserving permutation invariance. Additionally, we develop a low-latency Data Distribution Service-based communication protocol and an event-triggered deadlock resolution mechanism to enhance real-time coordination and prevent motion stagnation in constrained spaces. Our framework is validated through high-fidelity simulations in NVIDIA Omniverse Isaac Sim and real-world experiments using our custom quadrupedal robotic system, XG. Results demonstrate stable formation control, real-time feasibility, and effective collision avoidance, validating its potential for large-scale deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
