制御バリア関数による3Dガウススプラッティング場における競合認識型能動知覚と制御
Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions
3Dガウススプラッティングで表現された環境において、安全性と情報獲得の競合を扱う能動知覚・制御フレームワークを提案。CBFによる安全制約と知覚バリア関数による視点選択を統合したQPで解決する。
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著者: Amirhossein Mollaei Khass, Athanasios Cosse, Vivek Pandey, Nader Motee
分類: cs.RO
原文アブストラクト
Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These objectives inherently conflict, as informative viewpoints often lie near uncertain regions with higher collision risk. To address this challenge, we develop a conflict-aware active perception and control framework for robotic systems operating in environments represented by 3D Gaussian Splatting (3DGS). Safety is enforced using a Control Barrier Function (CBF) derived from an Average Value-at-Risk AV@R collision-risk metric that accounts for geometric uncertainty and guarantees forward invariance of a safe set. To improve perception, we propose a risk-aware Expected Information Gain (EIG) formulation for selecting the next-best-view and introduce perception barrier functions that align the camera orientation with the local information-ascent direction. To obtain a tractable formulation for these conflicting safety and perception objectives, we propose a unified safety-critical, perception-aware quadratic program that enforces safety as a hard constraint while relaxing perception constraints through slack variables. Simulation results demonstrate that the proposed method improves both safety and information acquisition compared to existing 3DGS-based approaches.
