融合风险规避与主动感知，实时优化安全导航路径。
Active Next-Best-View Optimization for Risk-Averse Path Planning
- 基于3D高斯溅射场构建风险地图，动态评估路径安全性。
- 在SE(3)流形上优化视角选择，显著降低关键区域不确定性。
- 适合复杂动态环境下的机器人自主导航系统使用。
在不确定环境中实现安全导航需要将风险规避与主动感知相结合。本文提出一种统一框架，通过在线更新的3D高斯溅射辐射场，利用平均价值在风险（Average Value-at-Risk）统计量构建尾部敏感的风险地图，从而精细化修正粗略参考路径，生成局部安全且可行的轨迹。同时，将下一最佳视角（Next-Best-View, NBV）选择建模为在SE(3)位姿流形上的优化问题，采用黎曼梯度下降最大化期望信息增益目标，以减少对即将发生运动最关键的不确定性。该方法通过耦合风险规避路径优化与NBV规划，推动了当前技术的进展，并引入可扩展的梯度分解策略，支持在复杂环境中高效在线更新。我们通过大量计算实验验证了所提框架的有效性。
原文摘要 · Abstract (English)
Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a coarse reference path by constructing tail-sensitive risk maps from Average Value-at-Risk statistics on an online-updated 3D Gaussian-splat Radiance Field. These maps enable the generation of locally safe and feasible trajectories. In parallel, we formulate Next-Best-View (NBV) selection as an optimization problem on the SE(3) pose manifold, where Riemannian gradient descent maximizes an expected information gain objective to reduce uncertainty most critical for imminent motion. Our approach advances the state-of-the-art by coupling risk-averse path refinement with NBV planning, while introducing scalable gradient decompositions that support efficient online updates in complex environments. We demonstrate the effectiveness of the proposed framework through extensive computational studies.
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