This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic (LTL) for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction is employed at the high-level navigation planner to estimate the dynamic obstacles' location. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner, while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate non-periodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics.

翻译：本研究提出了一种分层集成的框架，用于在存在动态障碍物和不平坦地形的部分可观测环境中实现双足运动的安任务与运动规划（TAMP）。高层任务规划器采用线性时序逻辑（LTL）进行机器人与环境之间的反应式博弈综合，并对导航安全性和任务完成提供形式化保证。为应对环境部分可观测性，高层导航规划器采用信念抽象方法估计动态障碍物位置。据此，综合动作规划器向中层运动规划器发送一组运动动作，同时融入基于运动过程降阶模型（ROM）的安全定理所提取的安全运动规范。运动规划器利用ROM设计安全准则及采样算法，生成能够精确跟踪高层动作的非周期运动计划。在低层，实现了基于角动量线性倒立摆模型的足部落点控制器，并将其与踝关节驱动的无源控制器集成，用于全身轨迹跟踪。为应对外部扰动，本研究还探讨了关键帧运动状态的安全序列组合，并通过可达性分析实现对扰动的鲁棒过渡。整体TAMP框架通过在Agility Robotics设计的双足步行机器人Cassie和Digit上的大量仿真与硬件实验得到验证。