UEREBot: 在无结构环境中和高速动态障碍物下学习安全四足运动
UEREBot: Learning Safe Quadrupedal Locomotion under Unstructured Environments and High-Speed Dynamic Obstacles
AI总结：
UEREBot通过分层框架实现无结构环境和高速动态障碍物下的安全四足运动，提升避障成功率与运动稳定性。
摘要：
四足机器人正在越来越多地部署在无结构环境中。在这些环境中安全运动需要长时间的目标进展、在不平地形上的通过性以及静态约束，并且需要对高速动态障碍物进行避障。单一系统无法同时完全满足这三个目标：基于规划的决策可能太慢，而纯粹的反应决策会牺牲目标进展和通过性。为了解决这一冲突，我们提出UEREBot（无结构环境反射逃避机器人），这是一种分层框架，将慢速规划与即时反射逃避分开，并在执行过程中协调它们。UEREBot将任务制定为一个约束最优控制问题的蓝图。它采用一个时空规划器，提供向目标的参考指导和威胁信号。然后，它使用一种威胁感知的手动交接，将导航和反射动作融合为一个名义命令，并使用控制屏障函数盾作为最终执行的保障。我们在Isaac Lab模拟中评估了UEREBot，并将其部署在配备 onboard 感知的Unitree Go2四足机器人上。在具有复杂静态结构和高速动态障碍物的多样化环境中，UEREBot比代表性的基线方法在避障成功率和更稳定的运动方面表现更好，同时保持了目标进展，展示了改进的安全-进展权衡。
英文摘要：
Quadruped robots are increasingly deployed in unstructured environments. Safe locomotion in these settings requires long-horizon goal progress, passability over uneven terrain and static constraints, and collision avoidance against high-speed dynamic obstacles. A single system cannot fully satisfy all three objectives simultaneously: planning-based decisions can be too slow, while purely reactive decisions can sacrifice goal progress and passability. To resolve this conflict, we propose UEREBot (Unstructured-Environment Reflexive Evasion Robot), a hierarchical framework that separates slow planning from instantaneous reflexive evasion and coordinates them during execution. UEREBot formulates the task as a constrained optimal control problem blueprint. It adopts a spatial--temporal planner that provides reference guidance toward the goal and threat signals. It then uses a threat-aware handoff to fuse navigation and reflex actions into a nominal command, and a control barrier function shield as a final execution safeguard. We evaluate UEREBot in Isaac Lab simulation and deploy it on a Unitree Go2 quadruped equipped with onboard perception. Across diverse environments with complex static structure and high-speed dynamic obstacles, UEREBot achieves higher avoidance success and more stable locomotion while maintaining goal progress than representative baselines, demonstrating improved safety--progress trade-offs.
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。
