不確実な混雑環境におけるリスク適応型CVaRバリア関数を用いた安全ナビゲーション
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
混雑環境でのロボットナビゲーションにおいて、条件付きバリュー・アット・リスクバリア関数に基づきリスクレベルを自動調整し、動的ゾーンベースのバリア関数で衝突可能性を評価することで、安全性と最適化の実現可能性を両立させる手法を提案した。
著者: Xinyi Wang, Taekyung Kim, Bardh Hoxha, Georgios Fainekos, Dimitra Panagou
分類: cs.RO
原文アブストラクト
Robot navigation in dynamic, crowded environments poses a significant challenge due to the inherent uncertainties in the obstacle model. In this work, we propose a risk-adaptive approach based on the Conditional Value-at-Risk Barrier Function (CVaR-BF), where the risk level is automatically adjusted to accept the minimum necessary risk, achieving a good performance in terms of safety and optimization feasibility under uncertainty. Additionally, we introduce a dynamic zone-based barrier function which characterizes the collision likelihood by evaluating the relative state between the robot and the obstacle. By integrating risk adaptation with this new function, our approach adaptively expands the safety margin, enabling the robot to proactively avoid obstacles in highly dynamic environments. Comparisons and ablation studies demonstrate that our method outperforms existing social navigation approaches, and validate the effectiveness of our proposed framework.
