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 experiments in simulation on both synthetic and real-world scans demonstrate the feasibility of our method, showing that the proposed approach achieves performance comparable to controllers that rely on perfect ground-truth information.

翻译：反应式控制能够优雅地协调移动机械臂的基座与机械臂的运动。然而，如何在避免引入昂贵规划的前提下，结合精确的环境表示以规避障碍物，仍是一个挑战。本研究提出ReMoSPLAT，一种基于二次规划公式的反应式控制器，用于移动机械臂操作，其利用高斯溅射表示进行避障。通过在优化公式中整合额外的约束与代价项，移动机械臂平台能够在杂乱场景中抵达目标末端执行器位姿，同时避开障碍物。我们研究了两种高效计算机器人与障碍物距离方法的权衡，比较了纯几何方法与基于栅格化的方法。在合成数据与真实场景扫描数据上的仿真实验验证了本方法的可行性，结果表明所提方法的性能可与依赖完美地面真值信息的控制器相媲美。