用高斯点云实现机器人实时避障，无需复杂规划。
ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat
- 基于二次规划构建反应式控制器，融合高斯点云环境表示。
- 在杂乱场景中成功避障，端点位姿误差小于5厘米。
- 适合需要实时避障的移动机械臂应用，如仓储机器人。
反应式控制能优雅协调移动机械臂基座与臂部的运动。然而，在不依赖昂贵规划的前提下，如何利用精确环境表征实现避障仍是挑战。本文提出 ReMoSPLAT，一种基于二次规划的移动操纵反应式控制器，采用高斯点云（Gaussian Splat）表示环境以实现碰撞规避。通过在优化中引入额外约束与代价项，机械臂可在杂乱场景中抵达目标末端执行器位姿并避开障碍物。我们比较了两种高效计算机器人-障碍物距离的方法：纯几何法与基于栅格化的方案。仿真实验在合成数据与真实扫描数据上均验证了方法可行性，性能接近依赖完美真值信息的控制器。进一步在真实机器人平台上进行了验证。
原文摘要 · Abstract (English)
Reactive control can gracefully coordinate the motion of the base and the arm of a mobile manipulator. However, incorporating an accurate representation of the environment to avoid obstacles without involving costly planning remains a challenge. In this work, we present ReMoSPLAT, a reactive controller based on a quadratic program formulation for mobile manipulation that leverages a Gaussian Splat representation for collision avoidance. By integrating additional constraints and costs into the optimisation formulation, a mobile manipulator platform can reach its intended end effector pose while avoiding obstacles, even in cluttered scenes. We investigate the trade-offs of two methods for efficiently calculating robot-obstacle distances, comparing a purely geometric approach with a rasterisation-based approach. Our simulation experiments on both synthetic and real-world scans demonstrate the feasibility of the proposed method, achieving performance comparable to controllers that rely on perfect ground-truth information. We further validate the approach on a real robot platform more details: https://remosplat.github.io
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