半静的環境における意味的安全ナビゲーションのための知覚-行動ループの閉じ方
Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments
オンラインで構築した密マップと物体の意味・一貫性推定を制御バリア関数に組み込み、モデル予測制御で環境変化に適応する安全なナビゲーションを実現した。
著者: Jingxing Qian, Siqi Zhou, Nicholas Jianrui Ren, Veronica Chatrath, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
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.
