We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of search-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.

翻译：我们提出Reactive Action and Motion Planner（RAMP），该方法融合了基于搜索与反应式运动规划策略的优势。本质上，RAMP是一种分层方法，采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执行。在桌面清理应用场景中，我们证明RAMP能快速在机器人配置空间中搜索路径，满足任务与机器人特定约束，并通过响应静态或动态障碍物保障安全性。RAMP通过多项关键创新实现卓越性能：直接利用机器人配置空间的符号距离函数（SDF）表示进行碰撞检测与反应控制。SDF的使用使得轨迹规划时碰撞代价的定义更为平滑，对确保轨迹跟踪过程中的安全性至关重要。此外，我们提出一种新型MPPI变体，结合矢量场轨迹跟踪器的安全保障，实现增量式实时全局轨迹规划。仿真结果表明，本方法生成的路径与传统及先进方法在总轨迹长度上相当，但计算速度快30倍。真实场景实验在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。