We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of sampling-based and reactive approaches for motion planning. In essence, RAMP is a hierarchical approach where a novel variant of a Model Predictive Path Integral (MPPI) controller is used to generate trajectories which are then followed asynchronously by a local vector field controller. We demonstrate, in the context of a table clearing application, that RAMP can rapidly find paths in the robot's configuration space, satisfy task and robot-specific constraints, and provide safety by reacting to static or dynamically moving obstacles. RAMP achieves superior performance through a number of key innovations: we use Signed Distance Function (SDF) representations directly from the robot configuration space, both for collision checking and reactive control. The use of SDFs allows for a smoother definition of collision cost when planning for a trajectory, and is critical in ensuring safety while following trajectories. In addition, we introduce a novel variant of MPPI which, combined with the safety guarantees of the vector field trajectory follower, performs incremental real-time global trajectory planning. Simulation results establish that our method can generate paths that are comparable to traditional and state-of-the-art approaches in terms of total trajectory length while being up to 30 times faster. Real-world experiments demonstrate the safety and effectiveness of our approach in challenging table clearing scenarios. Videos and code are available at: https://samsunglabs.github.io/RAMP-project-page/

翻译：摘要：我们提出反应式动作与运动规划器（RAMP），该算法融合了基于采样的方法与反应式方法在运动规划中的优势。本质上，RAMP采用分层架构，其中使用新型模型预测路径积分（MPPI）控制器生成轨迹，随后由局部矢量场控制器异步跟踪这些轨迹。在桌面清理应用场景下，我们证明RAMP能够快速在机器人构型空间中搜索路径，满足任务与机器人特定约束，并通过对静态或动态运动障碍物的实时反应保障安全性。RAMP通过多项关键创新实现卓越性能：我们直接利用机器人构型空间的有符号距离函数（SDF）表示，同时用于碰撞检测与反应控制。SDF的应用使得轨迹规划中的碰撞代价定义更加平滑，并在轨迹跟踪过程中对安全性保障至关重要。此外，我们引入新型MPPI变体，结合矢量场轨迹跟踪器的安全保证，实现增量式实时全局轨迹规划。仿真结果表明，该方法生成的路径在总轨迹长度上与经典及前沿方法相当，而计算速度提升达30倍。真实场景实验在具有挑战性的桌面清理任务中验证了本方法的安全性与有效性。视频与代码详见：https://samsunglabs.github.io/RAMP-project-page/