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.

翻译：四足机器人正越来越多地部署于非结构化环境中。在此类场景下实现安全运动需要满足三个要求：长期目标推进、在不平坦地形与静态约束下的可通行性，以及规避高速动态障碍物。单一系统无法同时完全满足这三个目标：基于规划的策略可能响应过慢，而纯反应式策略则可能牺牲目标推进与可通行性。为解决这一矛盾，我们提出了UEREBot（非结构化环境反射式规避机器人），一种分层框架，该框架将慢速规划与瞬时反射式规避分离，并在执行过程中协调二者。UEREBot将任务构建为一个约束最优控制问题蓝图。它采用一个时空规划器，为朝向目标的运动及威胁信号提供参考指引。随后，它通过一个威胁感知的切换机制，将导航动作与反射动作融合为名义指令，并采用控制屏障函数防护层作为最终执行保障。我们在Isaac Lab仿真环境中评估了UEREBot，并将其部署在搭载了机载感知系统的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，与代表性基线方法相比，UEREBot在保持目标推进的同时，实现了更高的规避成功率和更稳定的运动性能，展现了更优的安全性与行进效率的权衡。