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：此项研究提出一个安全任务和运动规划的分级综合框架(TAMP),用于在一个有动态障碍和地形不均的可部分观察环境中安全任务和运动规划。高级别任务规划员使用线性时间逻辑(LTL),用于机器人与环境之间的被动游戏合成,为导航安全和任务完成提供正式保障。为解决环境部分可视性,高级导航规划员采用信仰抽象,以估计动态障碍的位置。因此,一个综合行动计划员向中级运动规划员发送一组移动动作动作动作,同时纳入根据移动过程的减序模型(ROM)从安全定理中提取的安全动作定律。运动规划员使用ROM来设计安全标准和取样算法,以生成非定期运动计划,准确跟踪高层行动。在低水平上,基于角-运动线线性倾斜曲性笔式工作模型的脚部控制器被安装到中,并结合一个按脚部起动过敏性过动的过动控制器,用于根据移动过程的减序模型(ROM)从安全轨道模型(ROM)中提取的安全动作规范。 运动规划员使用ROMLM 进行外部演算分析,并进行外部稳定地分析,并进行内部升级分析,通过内部升级结构结构结构分析,并进行内部演算。</s>