Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlooking challenges when deployed in real-world environments where objects can appear, disappear, and shift over time. In this paper, we present a closed-loop perception-action pipeline that bridges this gap. Our system encodes an online-constructed dense map, along with object-level semantic and consistency estimates into a control barrier function (CBF) to regulate safe regions in the scene. A model predictive controller (MPC) leverages the CBF-based safety constraints to adapt its navigation behaviour, which is particularly crucial when potential scene changes occur. We test the system in simulations and real-world experiments to demonstrate the impact of semantic information and scene change handling on robot behavior, validating the practicality of our approach.

翻译：在变化环境中自主导航的机器人需要自适应导航策略以实现长期安全运行。尽管许多现代控制范式提供了理论保障，但它们通常假设已知的外在安全约束，忽视了在现实环境中物体可能随时间出现、消失或移动的挑战。本文提出了一种闭环感知-行动管线，填补了这一空白。该系统将在线构建的稠密地图与物体级语义及一致性估计编码为控制障碍函数，以调控场景中的安全区域。模型预测控制器利用基于控制障碍函数的安全约束自适应调整导航行为——这在潜在场景变化发生时尤为关键。我们通过仿真和真实世界实验验证了语义信息及场景变化处理对机器人行为的影响，证实了该方法的实用性。