Abstract
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.
Abstract (translated)
自主机器人导航在变化环境中需要适应性导航策略来实现安全长期操作。虽然许多现代控制范式提供了理论保证，但它们通常假定已知的外部安全约束，忽视了在现实环境中物体可能出现、消失和移动的事实挑战。在本文中，我们提出了一个端到端的感知-动作管道，弥合了这一空白。我们的系统编码了一个在线构建的密集地图以及物体级别的语义和一致性估计，作为一个控制障碍函数（CBF）来调节场景中的安全区域。一个模型预测控制器（MPC）利用基于CBF的安全约束来适应其导航行为，尤其是在可能发生场景变化时更是至关重要。我们在仿真和现实实验中测试了系统，以证明语义信息和场景变化处理对机器人行为的影响，验证了我们对方法的实用性。
URL
https://arxiv.org/abs/2404.14546
