让机器人在3D场景中安全规划路径，避免碰撞
Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat
- 基于高斯点云构建风险感知的路径优化算法
- 在复杂环境中生成零碰撞轨迹，实测优于现有方法
- 适合机器人导航与机械臂操作等实际应用
神经辐射场和高斯点云最近推动了计算机视觉的发展，实现了复杂场景的逼真建模。然而，它们在机器人任务如路径规划中的应用仍受限，主要因为难以判断与辐射场的碰撞风险，且密集模型计算开销大。本文提出SPLANNING，一种在高斯点云模型中运行的风险感知路径优化器。首先推导出机器人与辐射场之间碰撞概率的严格上界；其次引入归一化重构的高斯点云，实现该碰撞上界的高效计算；最后提出一种可避免碰撞的轨迹优化方法。实验表明，SPLANNING在杂乱环境中生成无碰撞路径的表现优于当前最优方法。系统还在真实机器人机械臂上进行了测试。项目页面见 https://roahmlab.github.io/splanning。
原文摘要 · Abstract (English)
Neural Radiance Fields and Gaussian Splatting have recently transformed computer vision by enabling photo-realistic representations of complex scenes. However, they have seen limited application in real-world robotics tasks such as trajectory optimization. This is due to the difficulty in reasoning about collisions in radiance models and the computational complexity associated with operating in dense models. This paper addresses these challenges by proposing SPLANNING, a risk-aware trajectory optimizer operating in a Gaussian Splatting model. This paper first derives a method to rigorously upper-bound the probability of collision between a robot and a radiance field. Then, this paper introduces a normalized reformulation of Gaussian Splatting that enables efficient computation of this collision bound. Finally, this paper presents a method to optimize trajectories that avoid collisions in a Gaussian Splat. Experiments show that SPLANNING outperforms state-of-the-art methods in generating collision-free trajectories in cluttered environments. The proposed system is also tested on a real-world robot manipulator. A project page is available at https://roahmlab.github.io/splanning.
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