面向腿足机器人避障共享自主的遥操作者感知与安全关键自适应非线性MPC
Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
AI总结：
针对遥操作四足机器人杂乱环境避障，提出融合Boltzmann意图在线学习、PGD参数自适应与CBF安全约束的分层ANMPC框架，并通过Go2实验验证。
摘要：
确保人类与自主腿足机器人之间安全有效的协作是共享自主领域的一项根本挑战，对于在杂乱环境中导航的遥操作系统尤其如此。传统共享控制方法通常依赖固定混合策略，无法刻画腿足运动的动力学，并可能危及安全。本文提出一种遥操作者感知、安全关键的自适应非线性模型预测控制（ANMPC）框架，用于四足机器人在避障任务中的共享自主。该框架在人类与机器人动作之间采用固定仲裁权重，但通过使用噪声理性Boltzmann模型对人类输入建模来增强该方案；该模型的参数根据观测到的操纵杆指令，利用投影梯度下降（PGD）律在线自适应。安全性通过集成到计算高效NMPC中的控制屏障函数（CBF）约束来强制执行，从而在人类行为存在不确定性的情况下确保安全集的正向不变性。控制架构采用分层结构：基于CBF的高层ANMPC（10 Hz）生成人机混合速度参考；考虑动力学的中层NMPC（60 Hz）施加降阶单刚体（SRB）动力学以跟踪这些参考；低层非线性全身控制器（500 Hz）通过二次规划施加全阶动力学，以跟踪中层轨迹。在Unitree Go2四足机器人上进行的大量数值与硬件实验以及用户研究验证了该框架，展示了实时避障、人类意图参数在线学习以及安全的遥操作者协作。
英文摘要：
Ensuring safe and effective collaboration between humans and autonomous legged robots is a fundamental challenge in shared autonomy, particularly for teleoperated systems navigating cluttered environments. Conventional shared-control approaches often rely on fixed blending strategies that fail to capture the dynamics of legged locomotion and may compromise safety. This paper presents a teleoperator-aware, safety-critical, adaptive nonlinear model predictive control (ANMPC) framework for shared autonomy of quadrupedal robots in obstacle-avoidance tasks. The framework employs a fixed arbitration weight between human and robot actions but enhances this scheme by modeling the human input with a noisily rational Boltzmann model, whose parameters are adapted online using a projected gradient descent (PGD) law from observed joystick commands. Safety is enforced through control barrier function (CBF) constraints integrated into a computationally efficient NMPC, ensuring forward invariance of safe sets despite uncertainty in human behavior. The control architecture is hierarchical: a high-level CBF-based ANMPC (10 Hz) generates blended human-robot velocity references, a mid-level dynamics-aware NMPC (60 Hz) enforces reduced-order single rigid body (SRB) dynamics to track these references, and a low-level nonlinear whole-body controller (500 Hz) imposes the full-order dynamics via quadratic programming to track the mid-level trajectories. Extensive numerical and hardware experiments, together with a user study, on a Unitree Go2 quadrupedal robot validate the framework, demonstrating real-time obstacle avoidance, online learning of human intent parameters, and safe teleoperator collaboration.
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