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arXiv · 2404.14546

Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments

Jingxing Qian·Siqi Zhou·Nicholas Jianrui Ren·Veronica Chatrath·Angela P. Schoellig
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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.

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Jingxing Qian, Siqi Zhou, Nicholas Jianrui Ren, Veronica Chatrath, Angela P. Schoellig. 2024-04-22. Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments. https://arxiv.org/abs/2404.14546

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