信号时序逻辑引导的模型预测控制实现抗运行时外部扰动的鲁棒双足行走
Signal Temporal Logic-Guided Model Predictive Control for Robust Bipedal Locomotion Resilient to Runtime External Perturbations
AI总结：
本文提出一种基于信号时序逻辑引导的模型预测控制框架，通过编码任务规范生成具有形式化保证的最优控制序列，有效解决双足机器人在不可预见外部扰动下的恢复与运动稳定性问题。
摘要：
本研究探讨基于形式化方法的双足运动轨迹优化（TO），聚焦机器人遭遇不可预见外部扰动的场景。核心研究问题在于，确保双足机器人在外部扰动下的任务规范正确性并最大化规范鲁棒性。我们的贡献是设计了一个基于优化的任务与运动规划框架，生成具有外部扰动恢复形式化保证的最优控制序列。作为核心组件，模型预测控制器（MPC）将基于信号时序逻辑（STL）的任务规范编码为代价函数。特别地，我们研究了机器人承受侧向扰动的挑战场景，该扰动因腿部自碰撞增加失败风险。为此，我们合成了敏捷安全的交叉腿部机动动作以增强运动稳定性。本工作首次将STL提供的形式化保证纳入双足运动扰动恢复的TO中。我们通过仿真扰动实验验证了框架的有效性。
英文摘要：
This study investigates formal-method-based trajectory optimization (TO) for bipedal locomotion, focusing on scenarios where the robot encounters external perturbations at unforeseen times. Our key research question centers around the assurance of task specification correctness and the maximization of specification robustness for a bipedal robot in the presence of external perturbations. Our contribution includes the design of an optimization-based task and motion planning framework that generates optimal control sequences with formal guarantees of external perturbation recovery. As a core component of the framework, a model predictive controller (MPC) encodes signal temporal logic (STL)-based task specifications as a cost function. In particular, we investigate challenging scenarios where the robot is subjected to lateral perturbations that increase the risk of failure due to leg self-collision. To address this, we synthesize agile and safe crossed-leg maneuvers to enhance locomotion stability. This work marks the first study to incorporate formal guarantees offered by STL into a TO for perturbation recovery of bipedal locomotion. We demonstrate the efficacy of the framework via perturbation experiments in simulations.
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